AI Post Transformers

This episode explores an FPGA-based time-to-digital converter that combines careful delay-line layout with machine-learning-based calibration to achieve very fine timing measurements on real hardware. It explains how tapped-delay-line TDCs work, why real FPGA implementations suffer from nonuniform time bins and bubble errors, and why those imperfections matter for applications like LiDAR, medical imaging, particle physics, and high-speed communications. The discussion compares the new approach against earlier FPGA TDC work, arguing that the real contribution is not flashy AI but a practical learned decoder that maps a 940-bit raw hardware output into a more accurate time estimate after physical design has reduced as much noise as possible. Listeners would find it interesting because it gets specific about where machine learning genuinely helps in instrumentation: not replacing physics, but reducing calibration effort while preserving picosecond-level precision.

Sources:

1. Machine Learning Self-Calibrated FPGA Time-to-Digital Converter

https://podcast.do-not-panic.com/uploaded-pdfs/2026-05-08T03-07-35-153Z-1-s2.0-S2667305326000190-main.pdf

2. A 19.6 ps, FPGA-Based TDC With Multiple Channels for Open Source Applications — Matthew W. Fishburn, L. Harmen Menninga, Claudio Favi, Edoardo Charbon, 2013

https://scholar.google.com/scholar?q=A+19.6+ps%2C+FPGA-Based+TDC+With+Multiple+Channels+for+Open+Source+Applications

3. A low nonlinearity, missing-code free time-to-digital converter based on 28nm FPGAs with embedded bin-width calibrations — Haochang Chen, Yongliang Zhang, David Day-Uei Li, 2017

https://scholar.google.com/scholar?q=A+low+nonlinearity%2C+missing-code+free+time-to-digital+converter+based+on+28nm+FPGAs+with+embedded+bin-width+calibrations

4. A 19 ps Precision and 170 M Samples/s Time-to-Digital Converter Implemented in FPGA with Online Calibration — Mengdi Zhang, Ye Zhao, Zhengsheng Han, Fazhan Zhao, 2022

https://scholar.google.com/scholar?q=A+19+ps+Precision+and+170+M+Samples%2Fs+Time-to-Digital+Converter+Implemented+in+FPGA+with+Online+Calibration

5. Low-Resource Time-to-Digital Converters for Field Programmable Gate Arrays: A Review — Diego Real, David Calvo, 2024

https://scholar.google.com/scholar?q=Low-Resource+Time-to-Digital+Converters+for+Field+Programmable+Gate+Arrays%3A+A+Review

6. Calibration Methods for Time-to-Digital Converters — Wassim Khaddour, Wilfried Uhring, Foudil Dadouche, Norbert Dumas, Morgan Madec, 2023

https://scholar.google.com/scholar?q=Calibration+Methods+for+Time-to-Digital+Converters

7. Time Resolution Improvement Using Dual Delay Lines for Field-Programmable-Gate-Array-Based Time-to-Digital Converters with Real-Time Calibration — Yuan-Ho Chen, 2019

https://scholar.google.com/scholar?q=Time+Resolution+Improvement+Using+Dual+Delay+Lines+for+Field-Programmable-Gate-Array-Based+Time-to-Digital+Converters+with+Real-Time+Calibration

8. Novel machine learning-driven optimizing decoding solutions for FPGA-based time-to-digital converters — Fabio Garzetti, Nicola Lusardi, Enrico Ronconi, Andrea Costa, Angelo Geraci, 2024

https://scholar.google.com/scholar?q=Novel+machine+learning-driven+optimizing+decoding+solutions+for+FPGA-based+time-to-digital+converters

9. A novel FPGA-based time-to-digital converter featuring machine learning-aided self-calibration — Arash Amini Bardpareh, Eleonora Vacca, Davide Nicolini, Corrado De Sio, Sarah Azimi, Luca Sterpone, Elisa Fiorina, Emanuele Maria Data, Felix Mas Milian, 2026

https://scholar.google.com/scholar?q=A+novel+FPGA-based+time-to-digital+converter+featuring+machine+learning-aided+self-calibration

10. Multiple-tapped-delay-line hardware-linearisation technique based on wire load regulation — Dariusz Chaberski, Robert Frankowski, Marek Zielinski, Lukasz Zaworski, 2016

https://scholar.google.com/scholar?q=Multiple-tapped-delay-line+hardware-linearisation+technique+based+on+wire+load+regulation

11. 5.7 ps Resolution Time-to-Digital Converter Implementation Using Routing Path Delays — Roza Teklehaimanot Siecha, Getachew Alemu, Jeffrey Prinzie, Paul Leroux, 2023

https://scholar.google.com/scholar?q=5.7+ps+Resolution+Time-to-Digital+Converter+Implementation+Using+Routing+Path+Delays

12. Tapped delay line for compact time-to-digital converter on UltraScale FPGA and its coding method — Min Zhu, Xihan Qi, Tang Cui, Qiang Gao, 2023

https://scholar.google.com/scholar?q=Tapped+delay+line+for+compact+time-to-digital+converter+on+UltraScale+FPGA+and+its+coding+method

13. A High-Resolution (<10 ps RMS) 48-Channel Time-to-Digital Converter (TDC) Implemented in a Field Programmable Gate Array (FPGA) — Eugen Bayer, Michael Traxler, 2011

https://scholar.google.com/scholar?q=A+High-Resolution+%28%3C10+ps+RMS%29+48-Channel+Time-to-Digital+Converter+%28TDC%29+Implemented+in+a+Field+Programmable+Gate+Array+%28FPGA%29

14. A Low Nonlinearity, Missing-Code Free Time-to-Digital Converter Based on 28-nm FPGAs With Embedded Bin-Width Calibrations — Haochang Chen, Yongliang Zhang, David Day-Uei Li, 2017

https://scholar.google.com/scholar?q=A+Low+Nonlinearity%2C+Missing-Code+Free+Time-to-Digital+Converter+Based+on+28-nm+FPGAs+With+Embedded+Bin-Width+Calibrations

15. High-Performance Time-to-Digital Converter IP-Core for Xilinx Ultrascale/Ultrascale+ FPGAs — Nicola Lusardi, Fabio Garzetti, E. Ronconi, Nicola Corna, Andrea Costa, Angelo Geraci, 2022

https://scholar.google.com/scholar?q=High-Performance+Time-to-Digital+Converter+IP-Core+for+Xilinx+Ultrascale%2FUltrascale%2B+FPGAs

16. An FPGA-Based Time-to-Digital Converter With Online Dual-Chain Calibration — authors unclear from snippet, recent; exact year unclear

https://scholar.google.com/scholar?q=An+FPGA-Based+Time-to-Digital+Converter+With+Online+Dual-Chain+Calibration

17. A review of advancements and trends in time-to-digital converters based on FPGA — authors unclear from snippet, recent; exact year unclear

https://scholar.google.com/scholar?q=A+review+of+advancements+and+trends+in+time-to-digital+converters+based+on+FPGA

18. A study on the effect of temperature variations on FPGA-based multi-channel time-to-digital converters — authors unclear from snippet, recent; exact year unclear

https://scholar.google.com/scholar?q=A+study+on+the+effect+of+temperature+variations+on+FPGA-based+multi-channel+time-to-digital+converters

19. Heterogeneous Tapped Delay-Line Time-to-Digital Converter on Artix-7 FPGA — authors unclear from snippet, recent; exact year unclear

https://scholar.google.com/scholar?q=Heterogeneous+Tapped+Delay-Line+Time-to-Digital+Converter+on+Artix-7+FPGA

20. A New Implementation of Tapped-Delay-Line Based High-Performance Time-to-Digital Converter on Xilinx Kintex-7 FPGA — authors unclear from snippet, recent; exact year unclear

https://scholar.google.com/scholar?q=A+New+Implementation+of+Tapped-Delay-Line+Based+High-Performance+Time-to-Digital+Converter+on+Xilinx+Kintex-7+FPGA

21. NN-Based Encoding Applied to a Time-to-Digital Converter for Time Interval Measurements — authors unclear from snippet, recent; exact year unclear

https://scholar.google.com/scholar?q=NN-Based+Encoding+Applied+to+a+Time-to-Digital+Converter+for+Time+Interval+Measurements

22. AI Post Transformers: FPGA Neural Network Accelerators for Space — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-26-fpga-neural-network-accelerators-for-spa-3087ae.mp3

23. AI Post Transformers: Caffeine: A Unified FPGA for CNNs — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-06-caffeine-a-unified-fpga-for-cnns-e8acbe.mp3

Interactive Visualization: Machine Learning Self-Calibrated FPGA Time-to-Digital Converter

This episode explores the TensorFlow paper as a systems argument for unifying the full machine learning lifecycle, from mobile inference to large-scale distributed training, within a single stateful dataflow framework. It explains how TensorFlow represents computation as graphs with mutable state, why that mattered for device placement, parameter storage, checkpointing, and heterogeneous hardware, and how it aimed to improve on the limitations of DistBelief. The discussion also places the paper in the broader lineage of MapReduce, Dryad, Naiad, and parameter-server training, while debating whether TensorFlow truly generalized machine learning workflows or mainly fit the kinds of static, graph-friendly workloads large organizations like Google already needed. Listeners would find it interesting for its mix of technical history, distributed systems insight, and a clear-eyed look at the tradeoff between organizational scale, portability, and usability for everyday researchers.

Sources:

1. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems — Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viegas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, Xiaoqiang Zheng, 2016

http://arxiv.org/abs/1603.04467

2. MapReduce: Simplified Data Processing on Large Clusters — Jeffrey Dean and Sanjay Ghemawat, 2004

https://scholar.google.com/scholar?q=MapReduce%3A+Simplified+Data+Processing+on+Large+Clusters

3. Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks — Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, and Dennis Fetterly, 2007

https://scholar.google.com/scholar?q=Dryad%3A+Distributed+Data-Parallel+Programs+from+Sequential+Building+Blocks

4. Large Scale Distributed Deep Networks — Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Quoc V. Le, Mark Z. Mao, Marc'Aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, and Andrew Y. Ng, 2012

https://scholar.google.com/scholar?q=Large+Scale+Distributed+Deep+Networks

5. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems — Martín Abadi, Ashish Agarwal, Paul Barham, Jeffrey Dean, Rajat Monga, and many others, 2016

https://scholar.google.com/scholar?q=TensorFlow%3A+Large-Scale+Machine+Learning+on+Heterogeneous+Distributed+Systems

6. Naiad: A Timely Dataflow System — Frank McSherry, Derek G. Murray, Rebecca Isaacs, and Michael Isard, 2013

https://scholar.google.com/scholar?q=Naiad%3A+A+Timely+Dataflow+System

7. Project Adam: Building an Efficient and Scalable Deep Learning Training System — Trishul Chilimbi, Yutaka Suzue, Johnson Apacible, and Karthik Kalyanaraman, 2014

https://scholar.google.com/scholar?q=Project+Adam%3A+Building+an+Efficient+and+Scalable+Deep+Learning+Training+System

8. Parameter Server for Distributed Machine Learning — Mu Li, David G. Andersen, Alexander J. Smola, and Kai Yu, 2014

https://scholar.google.com/scholar?q=Parameter+Server+for+Distributed+Machine+Learning

9. Caffe: Convolutional Architecture for Fast Feature Embedding — Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell, 2014

https://scholar.google.com/scholar?q=Caffe%3A+Convolutional+Architecture+for+Fast+Feature+Embedding

10. Theano: A CPU and GPU Math Compiler in Python — James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley, and Yoshua Bengio, 2010

https://scholar.google.com/scholar?q=Theano%3A+A+CPU+and+GPU+Math+Compiler+in+Python

11. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems — Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang, 2015

https://scholar.google.com/scholar?q=MXNet%3A+A+Flexible+and+Efficient+Machine+Learning+Library+for+Heterogeneous+Distributed+Systems

12. SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement — Shihong Gao, Yiming Li, Xin Zhang, Yanyan Shen, Yingxia Shao, Lei Chen, 2024

https://scholar.google.com/scholar?q=SIMPLE%3A+Efficient+Temporal+Graph+Neural+Network+Training+at+Scale+with+Dynamic+Data+Placement

13. Strategy-Switch: From All-Reduce to Parameter Server for Faster Efficient Training — Nikodimos Provatas, Iasonas Chalas, Ioannis Konstantinou, Nectarios Koziris, 2025

https://scholar.google.com/scholar?q=Strategy-Switch%3A+From+All-Reduce+to+Parameter+Server+for+Faster+Efficient+Training

14. Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models — Longteng Zhang, Xiang Liu, Zeyu Li, Xinglin Pan, Peijie Dong, Ruibo Fan, Rui Guo, Xin Wang, Qiong Luo, Shaohuai Shi, Xiaowen Chu, 2023

https://scholar.google.com/scholar?q=Dissecting+the+Runtime+Performance+of+the+Training%2C+Fine-tuning%2C+and+Inference+of+Large+Language+Models

15. AI Post Transformers: ONNX Ecosystem, Optimization, and Deployment — Hal Turing & Dr. Ada Shannon, 2025

https://podcast.do-not-panic.com/episodes/onnx-ecosystem-optimization-and-deployment/

Interactive Visualization: TensorFlow for Distributed Machine Learning Systems

This episode explores SGLang, a system for making complex language model workflows run faster by treating them as full programs rather than single prompt-response calls. It explains how modern LLM applications involve branching, tool use, retries, and structured outputs, then examines SGLang’s co-design of a Python-embedded language with a specialized runtime that can optimize those patterns directly. The discussion highlights ideas like KV-cache reuse through RadixAttention, grammar-constrained decoding for reliable JSON output, and why these systems techniques matter more than just nicer prompt scripting. Listeners would find it interesting because it connects practical agent-style LLM engineering to deeper questions about compilers, serving infrastructure, and whether headline speedups really hold across real workloads.

Sources:

1. SGLang: Efficient Execution of Structured Language Model Programs — Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Chuyue Sun, Jeff Huang, Cody Hao Yu, Shiyi Cao, Christos Kozyrakis, Ion Stoica, Joseph E. Gonzalez, Clark Barrett, Ying Sheng, 2023

http://arxiv.org/abs/2312.07104

2. Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning — Saibo Geng, Martin Josifoski, Maxime Peyrard, Robert West, 2023

https://scholar.google.com/scholar?q=Grammar-Constrained+Decoding+for+Structured+NLP+Tasks+without+Finetuning

3. SGLang: Efficient Execution of Structured Language Model Programs — Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Chuyue Sun, Jeff Huang, Cody Hao Yu, Shiyi Cao, Christos Kozyrakis, Ion Stoica, Joseph E. Gonzalez, Clark Barrett, Ying Sheng, 2024

https://scholar.google.com/scholar?q=SGLang%3A+Efficient+Execution+of+Structured+Language+Model+Programs

4. XGrammar: Flexible and Efficient Structured Generation Engine for Large Language Models — Yixin Dong, Charlie F. Ruan, Yaxing Cai, Ruihang Lai, Ziyi Xu, Yilong Zhao, Tianqi Chen, 2024

https://scholar.google.com/scholar?q=XGrammar%3A+Flexible+and+Efficient+Structured+Generation+Engine+for+Large+Language+Models

5. Generating Structured Outputs from Language Models: Benchmark and Studies — Saibo Geng, Hudson Cooper, Michał Moskal, Samuel Jenkins, Julian Berman, Nathan Ranchin, Robert West, Eric Horvitz, Harsha Nori, 2025

https://scholar.google.com/scholar?q=Generating+Structured+Outputs+from+Language+Models%3A+Benchmark+and+Studies

6. Language Model Cascades — David Dohan, Winnie Xu, Aitor Lewkowycz, Jacob Austin, David Bieber, Raphael Gontijo Lopes, Yuhuai Wu, Henryk Michalewski, Rif A. Saurous, Jascha Sohl-Dickstein, Kevin Murphy, Charles Sutton, 2022

https://scholar.google.com/scholar?q=Language+Model+Cascades

7. Prompting Is Programming: A Query Language for Large Language Models — Luca Beurer-Kellner, Marc Fischer, Martin Vechev, 2023

https://scholar.google.com/scholar?q=Prompting+Is+Programming%3A+A+Query+Language+for+Large+Language+Models

8. DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines — Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T. Joshi, Hanna Moazam, Heather Miller, Matei Zaharia, Christopher Potts, 2023

https://scholar.google.com/scholar?q=DSPy%3A+Compiling+Declarative+Language+Model+Calls+into+Self-Improving+Pipelines

9. vLLM: Easy, Fast, and Cheap LLM Serving with PagedAttention — Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, et al., 2023

https://scholar.google.com/scholar?q=vLLM%3A+Easy%2C+Fast%2C+and+Cheap+LLM+Serving+with+PagedAttention

10. Guidance: A Guidance Language for Controlling Large Language Models — Microsoft Research and collaborators, 2023

https://scholar.google.com/scholar?q=Guidance%3A+A+Guidance+Language+for+Controlling+Large+Language+Models

11. LMQL: A Programming Language for Large Language Models — Luca Beurer-Kellner, Marc Fischer, Martin Vechev, 2023

https://scholar.google.com/scholar?q=LMQL%3A+A+Programming+Language+for+Large+Language+Models

12. Outlines — Thibault Glaunec and contributors, 2023

https://scholar.google.com/scholar?q=Outlines

13. KVLink: Accelerating Large Language Models via Efficient KV Cache Reuse — Jingbo Yang, Bairu Hou, Wei Wei, Yujia Bao, Shiyu Chang, 2025

https://scholar.google.com/scholar?q=KVLink%3A+Accelerating+Large+Language+Models+via+Efficient+KV+Cache+Reuse

14. Key, Value, Compress: A Systematic Exploration of KV Cache Compression Techniques — Neusha Javidnia, Bita Rouhani, Farinaz Koushanfar, 2025

https://scholar.google.com/scholar?q=Key%2C+Value%2C+Compress%3A+A+Systematic+Exploration+of+KV+Cache+Compression+Techniques

15. KV-CAR: KV Cache Compression using Autoencoders and KV Reuse in Large Language Models — Sourjya Roy, Shrihari Sridharan, Surya Selvam, Anand Raghunathan, 2025

https://scholar.google.com/scholar?q=KV-CAR%3A+KV+Cache+Compression+using+Autoencoders+and+KV+Reuse+in+Large+Language+Models

16. Grammar-Aligned Decoding — Kanghee Park, Jiayu Wang, Taylor Berg-Kirkpatrick, Nadia Polikarpova, Loris D'Antoni, 2024

https://scholar.google.com/scholar?q=Grammar-Aligned+Decoding

17. Grammar-Constrained Decoding Makes Large Language Models Better Logical Parsers — Federico Raspanti, Tanir Ozcelebi, Mike J. Holenderski, 2025

https://scholar.google.com/scholar?q=Grammar-Constrained+Decoding+Makes+Large+Language+Models+Better+Logical+Parsers

18. Marconi: Prefix Caching for the Era of Hybrid LLMs — Rui Pan, Zhuang Wang, Zhen Jia, Can Karakus, Luca Zancato, Tri Dao, Yida Wang, Ravi Netravali, 2025

https://scholar.google.com/scholar?q=Marconi%3A+Prefix+Caching+for+the+Era+of+Hybrid+LLMs

19. Towards Efficient Agents: A Co-Design of Inference Architecture and System — Weizhe Lin, Hui-Ling Zhen, Shuai Yang, Xian Wang, Renxi Liu, Hanting Chen, Wangze Zhang, Chuansai Zhou, Yiming Li, Chen Chen, Xing Li, Zhiyuan Yang, Xiaosong Li, Xianzhi Yu, Zhenhua Dong, Mingxuan Yuan, Yunhe Wang, 2025

https://scholar.google.com/scholar?q=Towards+Efficient+Agents%3A+A+Co-Design+of+Inference+Architecture+and+System

20. Optimizing Agentic Language Model Inference via Speculative Tool Calls — Daniel Nichols, Prajwal Singhania, Charles Jekel, Abhinav Bhatele, Harshitha Menon, 2025

https://scholar.google.com/scholar?q=Optimizing+Agentic+Language+Model+Inference+via+Speculative+Tool+Calls

21. AI Post Transformers: SGLang: Efficient Language Model Program Execution — Hal Turing & Dr. Ada Shannon, Sun,

https://podcast.do-not-panic.com/episodes/sglang-efficient-language-model-program-execution/

22. AI Post Transformers: Breaking the Prefix Barrier with Shared KV Cache — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-24-breaking-the-prefix-barrier-with-shared-a5e5a6.mp3

23. AI Post Transformers: Continuous Batching for LLM Inference: Throughput and Latency Gains — Hal Turing & Dr. Ada Shannon, Mon,

https://podcast.do-not-panic.com/episodes/continuous-batching-for-llm-inference-throughput-and-latency-gains/

24. AI Post Transformers: Speculative Decoding in Real vLLM Serving — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-04-speculative-decoding-in-real-vllm-servin-6f4e2b.mp3

25. AI Post Transformers: KV Cache TTL for Multi-Turn Agent Scheduling — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-09-kv-cache-ttl-for-multi-turn-agent-schedu-996bf1.mp3

26. AI Post Transformers: TokenDance for Multi-Agent KV Cache Sharing — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-22-tokendance-for-multi-agent-kv-cache-shar-aa9b99.mp3

Interactive Visualization: SGLang for Faster Structured LLM Programs

This episode explores CL-BENCH, a benchmark designed to test whether language models can actually learn task-specific knowledge from long, messy context and then reason with it, rather than merely retrieving facts or mimicking examples. It explains the distinction between long-context understanding, in-context learning, and the stronger notion of context learning, using examples like legal codes, product manuals, and experimental notebooks to show what real-world adaptation demands. The discussion highlights how the benchmark’s 500 contexts, 1,899 tasks, and dense binary verification rubrics are built to stress models on rule-following, procedural reasoning, and inferring governing relationships from data. Listeners would find it interesting because it gets at a central question in modern AI: whether bigger context windows actually make systems more capable, or just better at holding more text without truly learning from it.

Sources:

1. CL-bench: A Benchmark for Context Learning — Shihan Dou, Ming Zhang, Zhangyue Yin, Chenhao Huang, Yujiong Shen, Junzhe Wang, Jiayi Chen, Yuchen Ni, Junjie Ye, Cheng Zhang, Huaibing Xie, Jianglu Hu, Shaolei Wang, Weichao Wang, Yanling Xiao, Yiting Liu, Zenan Xu, Zhen Guo, Pluto Zhou, Tao Gui, Zuxuan Wu, Xipeng Qiu, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Di Wang, Shunyu Yao, 2026

http://arxiv.org/abs/2602.03587

2. Language Models are Few-Shot Learners — Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan and others, 2020

https://scholar.google.com/scholar?q=Language+Models+are+Few-Shot+Learners

3. MetaICL: Learning to Learn In Context — Sewon Min, Mike Lewis, Luke Zettlemoyer, Hannaneh Hajishirzi, 2021

https://scholar.google.com/scholar?q=MetaICL%3A+Learning+to+Learn+In+Context

4. Transformers learn in-context by gradient descent — Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, Joao Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, Max Vladymyrov, 2022

https://scholar.google.com/scholar?q=Transformers+learn+in-context+by+gradient+descent

5. Lost in the Middle: How Language Models Use Long Contexts — Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, Percy Liang, 2023

https://scholar.google.com/scholar?q=Lost+in+the+Middle%3A+How+Language+Models+Use+Long+Contexts

6. LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding — Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li, 2023

https://scholar.google.com/scholar?q=LongBench%3A+A+Bilingual%2C+Multitask+Benchmark+for+Long+Context+Understanding

7. BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack — Yurii Kuratov, Aydar Bulatov, Petr Anokhin, Ivan Rodkin, Dmitry Sorokin, Artyom Sorokin, Mikhail Burtsev, 2024

https://scholar.google.com/scholar?q=BABILong%3A+Testing+the+Limits+of+LLMs+with+Long+Context+Reasoning-in-a-Haystack

8. LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks — Yushi Bai, Shangqing Tu, Jiajie Zhang, Hao Peng, Xiaozhi Wang, Xin Lv, Shulin Cao, Jiazheng Xu, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li, 2024

https://scholar.google.com/scholar?q=LongBench+v2%3A+Towards+Deeper+Understanding+and+Reasoning+on+Realistic+Long-context+Multitasks

9. NoLiMa: Long-Context Evaluation Beyond Literal Matching — Ali Modarressi, Hanieh Deilamsalehy, Franck Dernoncourt, Trung Bui, Ryan A. Rossi, David Seunghyun Yoon, Hinrich Schutze, 2025

https://scholar.google.com/scholar?q=NoLiMa%3A+Long-Context+Evaluation+Beyond+Literal+Matching

10. LongReason: A Synthetic Long-Context Reasoning Benchmark via Context Expansion — Zhan Ling et al., 2025

https://scholar.google.com/scholar?q=LongReason%3A+A+Synthetic+Long-Context+Reasoning+Benchmark+via+Context+Expansion

11. DocPuzzle: A Process-Aware Benchmark for Evaluating Realistic Long-Context Reasoning Capabilities — Tianyi Zhuang et al., 2025

https://scholar.google.com/scholar?q=DocPuzzle%3A+A+Process-Aware+Benchmark+for+Evaluating+Realistic+Long-Context+Reasoning+Capabilities

12. In-Context Learning Creates Task Vectors — Roee Hendel, Mor Geva, Amir Globerson, 2023

https://scholar.google.com/scholar?q=In-Context+Learning+Creates+Task+Vectors

13. In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering — Sheng Liu, Haotian Ye, Lei Xing, James Zou, 2024

https://scholar.google.com/scholar?q=In-context+Vectors%3A+Making+In+Context+Learning+More+Effective+and+Controllable+Through+Latent+Space+Steering

14. Task Vectors in In-Context Learning: Emergence, Formation, and Benefit — Liu Yang, Ziqian Lin, Kangwook Lee, Dimitris Papailiopoulos, Robert Nowak, 2025

https://scholar.google.com/scholar?q=Task+Vectors+in+In-Context+Learning%3A+Emergence%2C+Formation%2C+and+Benefit

15. Learn to Memorize: Scalable Continual Learning in Semiparametric Models with Mixture-of-Neighbors Induction Memory — Guangyue Peng, Tao Ge, Wen Luo, Wei Li, Houfeng Wang, 2025

https://scholar.google.com/scholar?q=Learn+to+Memorize%3A+Scalable+Continual+Learning+in+Semiparametric+Models+with+Mixture-of-Neighbors+Induction+Memory

16. AI Post Transformers: How Induction Heads Emerge in Transformers — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-03-how-induction-heads-emerge-in-transforme-a7bfcb.mp3

17. AI Post Transformers: Real Context Size and Context Rot — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-07-real-context-size-and-context-rot-56cbb4.mp3

18. AI Post Transformers: DeepSeek-V4 and Practical Million-Token Context — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-25-deepseek-v4-and-practical-million-token-6f4de1.mp3

19. AI Post Transformers: In-Place Test-Time Training for Transformers — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-09-in-place-test-time-training-for-transfor-d0b976.mp3

20. AI Post Transformers: Training LLMs for Divide-and-Conquer Reasoning — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-04-training-llms-for-divide-and-conquer-rea-ea6e22.mp3

21. AI Post Transformers: Inverse IFEval: Unlearning LLM Cognitive Inertia — Hal Turing & Dr. Ada Shannon, 2025

https://podcast.do-not-panic.com/episodes/inverse-ifeval-unlearning-llm-cognitive-inertia/

Interactive Visualization: Can Models Learn from Long Context?

This episode explores the 1987 paper on synchronous data flow and how it turns stream-processing programs into analyzable graphs with fixed token production and consumption rates. It explains how those fixed rates let a compiler precompute a repeating execution schedule, prove steady-state consistency through balance equations, and allocate bounded buffers ahead of time instead of relying on expensive runtime scheduling. The discussion highlights why that tradeoff works so well for digital signal processing workloads like filtering, resampling, and codecs, while also showing why the model is too restrictive for messier software with irregular control flow. Listeners would find it interesting because it shows how a carefully limited programming model can unlock strong guarantees about performance, memory use, and parallel execution.

Sources:

1. Synchronous Data Flow for Signal Processing

https://ptolemy.berkeley.edu/publications/papers/87/synchdataflow/synchdataflow.pdf

2. Synchronous Data Flow — Edward A. Lee and David G. Messerschmitt, 1987

https://scholar.google.com/scholar?q=Synchronous+Data+Flow

3. Dataflow Process Networks — Edward A. Lee and Thomas M. Parks, 1995

https://scholar.google.com/scholar?q=Dataflow+Process+Networks

4. Cycle-Static Dataflow — Greet Bilsen, Marc Engels, Rudy Lauwereins, and Jean Peperstraete, 1996

https://scholar.google.com/scholar?q=Cycle-Static+Dataflow

5. Static Scheduling of Synchronous Data Flow Programs for Digital Signal Processing — Edward A. Lee and David G. Messerschmitt, 1987

https://scholar.google.com/scholar?q=Static+Scheduling+of+Synchronous+Data+Flow+Programs+for+Digital+Signal+Processing

6. Bounded Scheduling of Process Networks — Thomas M. Parks, 1995

https://scholar.google.com/scholar?q=Bounded+Scheduling+of+Process+Networks

7. Synthesis of Embedded Software from Synchronous Dataflow Specifications — Shuvra S. Bhattacharyya, Praveen K. Murthy, and Edward A. Lee, 1999

https://scholar.google.com/scholar?q=Synthesis+of+Embedded+Software+from+Synchronous+Dataflow+Specifications

8. StreamIt: A Language for Streaming Applications — William Thies, Michal Karczmarek, and Saman Amarasinghe, 2002

https://scholar.google.com/scholar?q=StreamIt%3A+A+Language+for+Streaming+Applications

9. Memory Management for Dataflow Programming of Multirate Signal Processing Algorithms — Shuvra S. Bhattacharyya and Edward A. Lee, 1994

https://scholar.google.com/scholar?q=Memory+Management+for+Dataflow+Programming+of+Multirate+Signal+Processing+Algorithms

10. Joint Minimization of Code and Data for Synchronous Dataflow Programs — Praveen K. Murthy, Shuvra S. Bhattacharyya, and Edward A. Lee, 1994

https://scholar.google.com/scholar?q=Joint+Minimization+of+Code+and+Data+for+Synchronous+Dataflow+Programs

11. Buffer Merging: A Powerful Technique for Reducing Memory Requirements of Synchronous Dataflow Specifications — Praveen K. Murthy and Shuvra S. Bhattacharyya, 2000

https://scholar.google.com/scholar?q=Buffer+Merging%3A+A+Powerful+Technique+for+Reducing+Memory+Requirements+of+Synchronous+Dataflow+Specifications

12. Pipeline Interleaved Programmable DSP's: Synchronous Data Flow Programming — Edward A. Lee and David G. Messerschmitt, 1987

https://scholar.google.com/scholar?q=Pipeline+Interleaved+Programmable+DSP%27s%3A+Synchronous+Data+Flow+Programming

13. Multirate Digital Filters, Filter Banks, Polyphase Networks, and Applications: A Tutorial — P. P. Vaidyanathan, 1990

https://scholar.google.com/scholar?q=Multirate+Digital+Filters%2C+Filter+Banks%2C+Polyphase+Networks%2C+and+Applications%3A+A+Tutorial

14. The Semantics of a Simple Language for Parallel Programming — Gilles Kahn, 1974

https://scholar.google.com/scholar?q=The+Semantics+of+a+Simple+Language+for+Parallel+Programming

15. First Version of a Data Flow Procedure Language — Jack B. Dennis, 1974

https://scholar.google.com/scholar?q=First+Version+of+a+Data+Flow+Procedure+Language

16. On the Boundedness of Process Networks — Gilles Kahn and David B. MacQueen, 1977

https://scholar.google.com/scholar?q=On+the+Boundedness+of+Process+Networks

17. Algorithm Design for Signal Processing — Charles S. Burrus, 1982

https://scholar.google.com/scholar?q=Algorithm+Design+for+Signal+Processing

18. DynVec: An End-to-End Framework for Efficient Vector-Dataflow Execution — approximate; recent systems/compiler authors, recent

https://scholar.google.com/scholar?q=DynVec%3A+An+End-to-End+Framework+for+Efficient+Vector-Dataflow+Execution

19. Compiler discovered dynamic scheduling of irregular code in high-level synthesis — approximate; recent HLS/compiler authors, recent

https://scholar.google.com/scholar?q=Compiler+discovered+dynamic+scheduling+of+irregular+code+in+high-level+synthesis

20. Dataflow Models of computation for programming heterogeneous multicores — approximate; recent embedded/parallel-systems authors, recent

https://scholar.google.com/scholar?q=Dataflow+Models+of+computation+for+programming+heterogeneous+multicores

21. Heuristic & Expert-Guided Buffer Sizing for Neural Network Inference Applications on FPGAs — approximate; recent FPGA/dataflow authors, recent

https://scholar.google.com/scholar?q=Heuristic+%26+Expert-Guided+Buffer+Sizing+for+Neural+Network+Inference+Applications+on+FPGAs

22. Sgcn: Exploiting compressed-sparse features in deep graph convolutional network accelerators — approximate; recent accelerator authors, recent

https://scholar.google.com/scholar?q=Sgcn%3A+Exploiting+compressed-sparse+features+in+deep+graph+convolutional+network+accelerators

23. Safe shared state in dataflow systems — approximate; recent programming-systems authors, recent

https://scholar.google.com/scholar?q=Safe+shared+state+in+dataflow+systems

24. An Intermediate Representation for Stateful Dataflows — approximate; recent systems authors, recent

https://scholar.google.com/scholar?q=An+Intermediate+Representation+for+Stateful+Dataflows

25. AI Post Transformers: FlatAttention for Tile-Based Accelerator Inference — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-04-flatattention-for-tile-based-accelerator-56e6ca.mp3

26. AI Post Transformers: Caffeine: A Unified FPGA for CNNs — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-06-caffeine-a-unified-fpga-for-cnns-e8acbe.mp3

27. AI Post Transformers: Caffe and the Rise of CNN Frameworks — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-06-caffe-and-the-rise-of-cnn-frameworks-cf15f3.mp3

Interactive Visualization: Synchronous Data Flow for Signal Processing

This episode explores FP-DNN, a 2017 framework that aims to compile TensorFlow-era neural networks onto FPGAs automatically, reducing the need for hand-designed accelerators for each model. It explains how the system maps convolutional layers, fully connected layers, and parts of LSTM computation into a shared matrix-multiplication core, while combining hand-tuned RTL for performance-critical components with HLS-generated logic for orchestration and layer-specific handling. The discussion highlights why this hybrid design matters for performance-per-watt, latency, and communication efficiency, especially as deeper CNNs and recurrent models were pushing hardware limits. Listeners would find it interesting for its clear look at an early attempt to turn FPGA deployment from an expert-only craft into a more reusable compiler-driven workflow, while also showing where the paper’s claims about broad model coverage may be too optimistic.

Sources:

1. Automating DNN Compilation for FPGA Accelerators

https://ceca.pku.edu.cn/media/lw/e3d0e0cd92452e0504b148220d442b9a.pdf

2. A Survey of FPGA-based Neural Network Inference Accelerators — Kaiyuan Guo, Shulin Zeng, Jincheng Yu, Yu Wang, Huazhong Yang, 2019

https://scholar.google.com/scholar?q=A+Survey+of+FPGA-based+Neural+Network+Inference+Accelerators

3. DeepBurning: Automatic Generation of FPGA-based Learning Accelerators for the Neural Network Family — Ying Wang, Jie Xu, Yudeng Sun, Baohua Cao, Chunyuan Xu, Yibo Kong, Chundao Han, Xuan Wang, 2016

https://scholar.google.com/scholar?q=DeepBurning%3A+Automatic+Generation+of+FPGA-based+Learning+Accelerators+for+the+Neural+Network+Family

4. FP-DNN: An Automated Framework for Mapping Deep Neural Networks onto FPGAs with RTL-HLS Hybrid Templates — Yijin Guan, Hao Liang, Ningyi Xu, Wenqiang Wang, Shaoshuai Shi, Xi Chen, Guangyu Sun, Wei Zhang, Jason Cong, 2017

https://scholar.google.com/scholar?q=FP-DNN%3A+An+Automated+Framework+for+Mapping+Deep+Neural+Networks+onto+FPGAs+with+RTL-HLS+Hybrid+Templates

5. DNNBuilder: An Automated Tool for Building High-Performance DNN Hardware Accelerators for FPGAs — Xiaofan Zhang, Junsong Wang, Chao Zhu, Yonghua Lin, Jinjun Xiong, Wen-Mei Hwu, Deming Chen, 2018

https://scholar.google.com/scholar?q=DNNBuilder%3A+An+Automated+Tool+for+Building+High-Performance+DNN+Hardware+Accelerators+for+FPGAs

6. From High-Level Deep Neural Models to FPGAs — Hardik Sharma, Jongse Park, Emmanuel Amaro, Bradley Thwaites, Priyanka Kotha, Anmol Gupta, Joon Kyung Kim, Asit Mishra, and Hsien-Hsin S. Lee, 2016

https://scholar.google.com/scholar?q=From+High-Level+Deep+Neural+Models+to+FPGAs

7. Caffeine: Towards Uniformed Representation and Acceleration for Deep Convolutional Neural Networks — Chen Zhang, Zhenman Fang, Peipei Zhou, Peichen Pan, and Jason Cong, 2016

https://scholar.google.com/scholar?q=Caffeine%3A+Towards+Uniformed+Representation+and+Acceleration+for+Deep+Convolutional+Neural+Networks

8. Throughput-Optimized OpenCL-Based FPGA Accelerator for Large-Scale Convolutional Neural Networks — Naveen Suda, Vikas Chandra, Ganesh Dasika, Abinash Mohanty, Yufei Ma, Sarita Vrudhula, Jae-sun Seo, and Yu Cao, 2016

https://scholar.google.com/scholar?q=Throughput-Optimized+OpenCL-Based+FPGA+Accelerator+for+Large-Scale+Convolutional+Neural+Networks

9. Going Deeper with Embedded FPGA Platform for Convolutional Neural Network — Jiantao Qiu, Jie Wang, Song Yao, Kai Guo, Boxun Li, Erjin Zhou, Jincheng Yu, Tianqi Tang, Ningyi Xu, and Song Wang, 2016

https://scholar.google.com/scholar?q=Going+Deeper+with+Embedded+FPGA+Platform+for+Convolutional+Neural+Network

10. Optimizing FPGA-Based Accelerator Design for Deep Convolutional Neural Networks — Chen Zhang, Peng Li, Guangyu Sun, Yijin Guan, Bingjun Xiao, and Jason Cong, 2015

https://scholar.google.com/scholar?q=Optimizing+FPGA-Based+Accelerator+Design+for+Deep+Convolutional+Neural+Networks

11. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding — Song Han, Huizi Mao, and William J. Dally, 2015

https://scholar.google.com/scholar?q=Deep+Compression%3A+Compressing+Deep+Neural+Networks+with+Pruning%2C+Trained+Quantization+and+Huffman+Coding

12. BladeDISC: Optimizing Dynamic Shape Machine Learning Workloads via Compiler Approach — Zhen Zheng et al., 2023

https://scholar.google.com/scholar?q=BladeDISC%3A+Optimizing+Dynamic+Shape+Machine+Learning+Workloads+via+Compiler+Approach

13. TSCompiler: efficient compilation framework for dynamic-shape models — Xiang Luo, Chen Zhang, Chenbo Geng, Yanzhi Yi, Jiahui Hu, Renwei Zhang, Zhen Zhang, Gianpietro Consolaro, Fan Yang, Tun Lu, Ning Gu, Li Shang, 2024

https://scholar.google.com/scholar?q=TSCompiler%3A+efficient+compilation+framework+for+dynamic-shape+models

14. TATAA: Programmable Mixed-Precision Transformer Acceleration with a Transformable Arithmetic Architecture — Jiajun Wu, Mo Song, Jingmin Zhao, Yizhao Gao, Jia Li, Hayden Kwok-Hay So, 2024

https://scholar.google.com/scholar?q=TATAA%3A+Programmable+Mixed-Precision+Transformer+Acceleration+with+a+Transformable+Arithmetic+Architecture

15. FPGA Acceleration With Hessian-Based Comprehensive Intra-Layer Mixed-Precision Quantization for Transformer Models — Woohong Byun, Jongseok Woo, Saibal Mukhopadhyay, 2025

https://scholar.google.com/scholar?q=FPGA+Acceleration+With+Hessian-Based+Comprehensive+Intra-Layer+Mixed-Precision+Quantization+for+Transformer+Models

16. Understand and Accelerate Memory Processing Pipeline for Disaggregated LLM Inference — Zifan He, Rui Ma, Yizhou Sun, Jason Cong, 2026

https://scholar.google.com/scholar?q=Understand+and+Accelerate+Memory+Processing+Pipeline+for+Disaggregated+LLM+Inference

17. AI Post Transformers: FPGA Neural Network Accelerators for Space — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-26-fpga-neural-network-accelerators-for-spa-3087ae.mp3

18. AI Post Transformers: Continuous Batching for LLM Inference: Throughput and Latency Gains — Hal Turing & Dr. Ada Shannon, 2025

https://podcast.do-not-panic.com/episodes/continuous-batching-for-llm-inference-throughput-and-latency-gains/

19. AI Post Transformers: Advancements in Efficient KV Cache Quantization and Management — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/advancements-in-efficient-kv-cache-quantization-and-management/

Interactive Visualization: Automating DNN Compilation for FPGA Accelerators

This episode explores why Caffe mattered as a systems breakthrough for the early CNN era, even though it did not introduce a new learning algorithm. It explains how the framework helped researchers and engineers move from handcrafted vision features to learned feature embeddings, and why separating model definition from implementation made experimentation and deployment far more practical. The discussion highlights Caffe’s use of declarative Protocol Buffers configurations, directed acyclic graph model structure, and the blob abstraction that hid CPU versus GPU details while supporting modular extensions. Listeners would find it interesting for its clear account of how deep learning became usable at scale in 2014, and for its nuanced take on Caffe’s evidence: strong engineering promises, impressive throughput figures, and a major role in shaping the emerging model-development ecosystem.

Sources:

1. Caffe: Convolutional Architecture for Fast Feature Embedding — Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, Trevor Darrell, 2014

http://arxiv.org/abs/1408.5093

2. ImageNet Classification with Deep Convolutional Neural Networks — Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, 2012

https://scholar.google.com/scholar?q=ImageNet+Classification+with+Deep+Convolutional+Neural+Networks

3. OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks — Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, Yann LeCun, 2013

https://scholar.google.com/scholar?q=OverFeat%3A+Integrated+Recognition%2C+Localization+and+Detection+using+Convolutional+Networks

4. Decaf: A Deep Convolutional Activation Feature for Generic Visual Recognition — Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, Trevor Darrell, 2014

https://scholar.google.com/scholar?q=Decaf%3A+A+Deep+Convolutional+Activation+Feature+for+Generic+Visual+Recognition

5. cuda-convnet — Alex Krizhevsky, 2012

https://scholar.google.com/scholar?q=cuda-convnet

6. Theano: new features and speed improvements — Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, James Bergstra, Ian Goodfellow, Arnaud Bergeron, Nicolas Bouchard, David Warde-Farley, Yoshua Bengio and others, 2012

https://scholar.google.com/scholar?q=Theano%3A+new+features+and+speed+improvements

7. Pylearn2: a machine learning research library — Ian Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua Bengio, 2013

https://scholar.google.com/scholar?q=Pylearn2%3A+a+machine+learning+research+library

8. Torch7 — Ronan Collobert, Koray Kavukcuoglu, Clément Farabet and others, 2011

https://scholar.google.com/scholar?q=Torch7

9. Efficient inference of Vision Transformer with structural pruning and operator fusion on GPU — unknown from snippet, recent

https://scholar.google.com/scholar?q=Efficient+inference+of+Vision+Transformer+with+structural+pruning+and+operator+fusion+on+GPU

10. I-ViT: Integer-only quantization for efficient vision transformer inference — unknown from snippet, recent

https://scholar.google.com/scholar?q=I-ViT%3A+Integer-only+quantization+for+efficient+vision+transformer+inference

11. DeViT: Decomposing vision transformers for collaborative inference in edge devices — unknown from snippet, recent

https://scholar.google.com/scholar?q=DeViT%3A+Decomposing+vision+transformers+for+collaborative+inference+in+edge+devices

12. Raman: A reconfigurable and sparse TinyML accelerator for inference on edge — unknown from snippet, recent

https://scholar.google.com/scholar?q=Raman%3A+A+reconfigurable+and+sparse+TinyML+accelerator+for+inference+on+edge

13. Hardware accelerator design for sparse DNN inference and training: A tutorial — unknown from snippet, recent

https://scholar.google.com/scholar?q=Hardware+accelerator+design+for+sparse+DNN+inference+and+training%3A+A+tutorial

14. Inference serving with end-to-end latency SLOs over dynamic edge networks — unknown from snippet, recent

https://scholar.google.com/scholar?q=Inference+serving+with+end-to-end+latency+SLOs+over+dynamic+edge+networks

15. Training data attribution via approximate unrolling — unknown from snippet, recent

https://scholar.google.com/scholar?q=Training+data+attribution+via+approximate+unrolling

16. Exploring Training Data Attribution under Limited Access Constraints — unknown from snippet, recent

https://scholar.google.com/scholar?q=Exploring+Training+Data+Attribution+under+Limited+Access+Constraints

17. DATE-LM: Benchmarking Data Attribution Evaluation for Large Language Models — unknown from snippet, recent

https://scholar.google.com/scholar?q=DATE-LM%3A+Benchmarking+Data+Attribution+Evaluation+for+Large+Language+Models

18. AI Post Transformers: Speculative Decoding in Real vLLM Serving — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-04-speculative-decoding-in-real-vllm-servin-6f4e2b.mp3

19. AI Post Transformers: GPT-NeoX: Large-Scale Autoregressive Language Modeling in PyTorch — Hal Turing & Dr. Ada Shannon, 2025

https://podcast.do-not-panic.com/episodes/gpt-neox-large-scale-autoregressive-language-modeling-in-pytorch/

20. AI Post Transformers: NVMe Offload on Colossal AI: Breaking the GPU Memory Wall — Hal Turing & Dr. Ada Shannon, 2025

https://podcast.do-not-panic.com/episodes/nvme-offload-on-colossal-ai-breaking-the-gpu-memory-wall/

21. AI Post Transformers: Mamba-3 for Efficient Sequence Modeling — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-16-mamba-3-for-efficient-sequence-modeling-97a22a.mp3

Interactive Visualization: Caffe and the Rise of CNN Frameworks

This episode explores the 2016 Caffeine FPGA accelerator and its central claim that a single FPGA design can handle an entire CNN efficiently, rather than excelling at convolutions while bottlenecking on fully connected layers. It explains why that mattered in the AlexNet-to-VGG era, when convolutional layers were compute-bound but dense layers often became communication-bound because moving weights and activations through memory was the real constraint. The discussion focuses on Caffeine’s main technical idea: a unified matrix-multiplication-oriented representation that supports both convolution and fully connected layers without the heavy data expansion of standard `im2col` approaches, plus memory-access scheduling choices such as weight-major mapping to improve reuse and burst efficiency. Listeners would find it interesting because the episode makes a precise systems argument about how hardware performance depends not just on arithmetic throughput, but on matching dataflow, buffering, and bandwidth to the structure of the network.

Sources:

1. Caffeine: A Unified FPGA for CNNs

https://ceca.pku.edu.cn/media/lw/83b308c75c56a94fbf706b92dbe57917.pdf

2. Gradient-Based Learning Applied to Document Recognition — Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, 1998

https://scholar.google.com/scholar?q=Gradient-Based+Learning+Applied+to+Document+Recognition

3. ImageNet Classification with Deep Convolutional Neural Networks — Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, 2012

https://scholar.google.com/scholar?q=ImageNet+Classification+with+Deep+Convolutional+Neural+Networks

4. Very Deep Convolutional Networks for Large-Scale Image Recognition — Karen Simonyan, Andrew Zisserman, 2014

https://scholar.google.com/scholar?q=Very+Deep+Convolutional+Networks+for+Large-Scale+Image+Recognition

5. Deep Residual Learning for Image Recognition — Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, 2016

https://scholar.google.com/scholar?q=Deep+Residual+Learning+for+Image+Recognition

6. DianNao: A Small-Footprint High-Throughput Accelerator for Ubiquitous Machine-Learning — Tianshi Chen, Zidong Du, Ninghui Sun, Jia Wang, Chengyong Wu, Yunji Chen, Olivier Temam, 2014

https://scholar.google.com/scholar?q=DianNao%3A+A+Small-Footprint+High-Throughput+Accelerator+for+Ubiquitous+Machine-Learning

7. Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks — Yu-Hsin Chen, Tushar Krishna, Joel S. Emer, Vivienne Sze, 2016

https://scholar.google.com/scholar?q=Eyeriss%3A+An+Energy-Efficient+Reconfigurable+Accelerator+for+Deep+Convolutional+Neural+Networks

8. Caffeine: Towards Uniformed Representation and Acceleration for Deep Convolutional Neural Networks — Chen Zhang, Zhenman Fang, Peipei Zhou, Peichen Pan, Jason Cong, 2016

https://scholar.google.com/scholar?q=Caffeine%3A+Towards+Uniformed+Representation+and+Acceleration+for+Deep+Convolutional+Neural+Networks

9. In-Datacenter Performance Analysis of a Tensor Processing Unit — Norman P. Jouppi and many colleagues at Google, 2017

https://scholar.google.com/scholar?q=In-Datacenter+Performance+Analysis+of+a+Tensor+Processing+Unit

10. Optimizing FPGA-Based Accelerator Design for Deep Convolutional Neural Networks — Chen Zhang, Peng Li, Guangyu Sun, Yijin Guan, Bingjun Xiao, Jason Cong, 2015

https://scholar.google.com/scholar?q=Optimizing+FPGA-Based+Accelerator+Design+for+Deep+Convolutional+Neural+Networks

11. Going Deeper with Embedded FPGA Platform for Convolutional Neural Network — Jiantao Qiu, Jingsheng Wang, Song Yao, Kaiyuan Guo, Boxun Li, Erjin Zhou, Jincheng Yu, Tianqi Tang, Ningyi Xu, Sen Song, Yu Wang, Huazhong Yang, 2016

https://scholar.google.com/scholar?q=Going+Deeper+with+Embedded+FPGA+Platform+for+Convolutional+Neural+Network

12. fpgaConvNet: A Framework for Mapping Convolutional Neural Networks on FPGAs — Stylianos I. Venieris, Christos-Savvas Bouganis, 2016

https://scholar.google.com/scholar?q=fpgaConvNet%3A+A+Framework+for+Mapping+Convolutional+Neural+Networks+on+FPGAs

13. A high-performance FPGA-based depthwise separable convolution accelerator — approximate; recent FPGA accelerator authors, recent

https://scholar.google.com/scholar?q=A+high-performance+FPGA-based+depthwise+separable+convolution+accelerator

14. Fpga-based acceleration for convolutional neural networks: A comprehensive review — approximate; review authors, recent

https://scholar.google.com/scholar?q=Fpga-based+acceleration+for+convolutional+neural+networks%3A+A+comprehensive+review

15. Mobile-X: Dedicated FPGA implementation of the MobileNet accelerator optimizing depthwise separable convolution — approximate; Mobile-X authors, recent

https://scholar.google.com/scholar?q=Mobile-X%3A+Dedicated+FPGA+implementation+of+the+MobileNet+accelerator+optimizing+depthwise+separable+convolution

16. Design of a convolutional neural network accelerator based on on-chip data reordering — approximate; accelerator authors, recent

https://scholar.google.com/scholar?q=Design+of+a+convolutional+neural+network+accelerator+based+on+on-chip+data+reordering

17. Energy-efficient and high-throughput CNN inference engine based on memory-sharing and data-reusing for edge applications — approximate; edge-CNN accelerator authors, recent

https://scholar.google.com/scholar?q=Energy-efficient+and+high-throughput+CNN+inference+engine+based+on+memory-sharing+and+data-reusing+for+edge+applications

18. An efficient sparse CNN inference accelerator with balanced intra-and inter-PE workload — approximate; sparse accelerator authors, recent

https://scholar.google.com/scholar?q=An+efficient+sparse+CNN+inference+accelerator+with+balanced+intra-and+inter-PE+workload

19. Hardware accelerator design for sparse DNN inference and training: A tutorial — approximate; tutorial authors, recent

https://scholar.google.com/scholar?q=Hardware+accelerator+design+for+sparse+DNN+inference+and+training%3A+A+tutorial

20. AI Post Transformers: FlatAttention for Tile-Based Accelerator Inference — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-04-flatattention-for-tile-based-accelerator-56e6ca.mp3

21. AI Post Transformers: RFNoC SISO Processor via High-Level Synthesis — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-06-rfnoc-siso-processor-via-high-level-synt-c892f3.mp3

22. AI Post Transformers: Computation-Bandwidth-Memory Trade-offs for AI Infrastructure — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-09-computation-bandwidth-memory-trade-offs-a83f2b.mp3

23. AI Post Transformers: Los Alamos: overcoming the memory wall fighting sparse memory access — Hal Turing & Dr. Ada Shannon, 2025

https://podcast.do-not-panic.com/episodes/los-alamos-overcoming-the-memory-wall-fighting-sparse-memory-access/

Interactive Visualization: Caffeine: A Unified FPGA for CNNs

This episode explores how the CMS experiment uses machine learning inside its Level-1 endcap muon trigger, where hardware must estimate muon momentum within roughly 500 nanoseconds while filtering an enormous stream of proton-collision data. It explains why boosted decision trees were chosen over neural networks: not because they are trendier, but because they fit strict FPGA constraints around deterministic latency, fixed-point arithmetic, and bounded memory. A central finding is that the online system does not run the trees directly; instead, the model is trained offline and compiled into a massive precomputed lookup table, turning inference into a single fast memory access. The discussion is especially interesting because it shows machine learning as a systems-and-hardware co-design problem, grounded in detector physics, feature engineering, and the practical realities of deploying learned functions in one of the harshest real-time environments in science.

Sources:

1. Boosted Decision Trees for CMS Muon Triggers

https://indico.cern.ch/event/567550/papers/2629686/files/6172-acat_bdt_l1t.pdf

2. Applications and Techniques for Fast Machine Learning in Science — Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Mia Liu, Mark Neubauer, Jennifer Ngadiuba, Maurizio Pierini and many others, 2022

https://scholar.google.com/scholar?q=Applications+and+Techniques+for+Fast+Machine+Learning+in+Science

3. Fast inference of Boosted Decision Trees in FPGAs for particle physics — Sioni Summers, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Duc Hoang, Sergo Jindariani, Edward Kreinar, Vladimir Loncar, Jennifer Ngadiuba, Maurizio Pierini, Dylan Rankin, Nhan Tran and Zhenbin Wu, 2020

https://scholar.google.com/scholar?q=Fast+inference+of+Boosted+Decision+Trees+in+FPGAs+for+particle+physics

4. Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors — Claudionor N. Coelho Jr, Aki Kuusela, Shan Li, Hao Zhuang, Jennifer Ngadiuba, Thea Klaeboe Aarrestad, Vladimir Loncar, Maurizio Pierini, Adrian Alan Pol and Sioni Summers, 2021

https://scholar.google.com/scholar?q=Automatic+heterogeneous+quantization+of+deep+neural+networks+for+low-latency+inference+on+the+edge+for+particle+detectors

5. Serving DNNs in Real Time at Datacenter Scale with Project Brainwave — Eric Chung, Jeremy Fowers, Kalin Ovtcharov, Michael Papamichael, Adrian Caulfield, Todd Massengill, Ming Liu, Mahdi Ghandi, Daniel Lo and others, 2018

https://scholar.google.com/scholar?q=Serving+DNNs+in+Real+Time+at+Datacenter+Scale+with+Project+Brainwave

6. The CMS Trigger System — CMS Collaboration, not specified in excerpt

https://scholar.google.com/scholar?q=The+CMS+Trigger+System

7. The CMS Endcap Muon Track Finder — CMS Collaboration or EMTF-related authors, not specified in excerpt

https://scholar.google.com/scholar?q=The+CMS+Endcap+Muon+Track+Finder

8. TMVA: Toolkit for Multivariate Data Analysis — Andreas Hoecker and collaborators, not specified in excerpt

https://scholar.google.com/scholar?q=TMVA%3A+Toolkit+for+Multivariate+Data+Analysis

9. Fast Machine Learning for Science: how accelerated hardware and software are enabling real-time data analysis at the edge — Javier Duarte and collaborators, 2022

https://scholar.google.com/scholar?q=Fast+Machine+Learning+for+Science%3A+how+accelerated+hardware+and+software+are+enabling+real-time+data+analysis+at+the+edge

10. hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices — Giuseppe Di Guglielmo, Javier Duarte and collaborators, 2021

https://scholar.google.com/scholar?q=hls4ml%3A+An+Open-Source+Codesign+Workflow+to+Empower+Scientific+Low-Power+Machine+Learning+Devices

11. End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs — Javier Campos, Zhen Dong, Javier Duarte, Nhan Tran, et al., 2023

https://scholar.google.com/scholar?q=End-to-end+codesign+of+Hessian-aware+quantized+neural+networks+for+FPGAs+and+ASICs

12. FPGA-QNN: Quantized Neural Network Hardware Acceleration on FPGAs — Mustafa Tasci, Ayhan Istanbullu, Vedat Tumen, Selahattin Kosunalp, 2025

https://scholar.google.com/scholar?q=FPGA-QNN%3A+Quantized+Neural+Network+Hardware+Acceleration+on+FPGAs

13. An FPGA-Based Time-to-Digital Converter with Online Dual-Chain Calibration — Zhengsen Jia, Yuzhuo Wang, Jie Ding, Qian Xu, et al., 2025

https://scholar.google.com/scholar?q=An+FPGA-Based+Time-to-Digital+Converter+with+Online+Dual-Chain+Calibration

14. A Novel FPGA-based Time-to-Digital Converter featuring Machine Learning-Aided Self-Calibration — Arash Amini Bardpareh, Eleonora Vacca, Davide Nicolini, Luca Sterpone, et al., 2026

https://scholar.google.com/scholar?q=A+Novel+FPGA-based+Time-to-Digital+Converter+featuring+Machine+Learning-Aided+Self-Calibration

15. AI Post Transformers: FlatAttention for Tile-Based Accelerator Inference — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-04-flatattention-for-tile-based-accelerator-56e6ca.mp3

16. AI Post Transformers: SolidAttention: Co-Designing Sparse Attention and SSD I/O — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-03-18-solidattention-co-designing-sparse-atten-5a8622.mp3

Interactive Visualization: Boosted Decision Trees for CMS Muon Triggers

This episode explores how a 2018 paper brings neural network inference into the Level-1 trigger at the Large Hadron Collider, where event decisions must be made under sub-microsecond latency constraints. It explains why FPGAs are a natural fit for this setting, emphasizing batch-one, deterministic inference and the hardware realities that make model size, timing, memory use, and routing just as important as accuracy. The discussion centers on a compact dense network for jet substructure classification, using 16 engineered features to distinguish quark, gluon, W, Z, and top jets while preserving rare physics signals. It also highlights the paper’s broader argument: tools like High-Level Synthesis and hls4ml can let physicists deploy hardware-aware ML workflows directly, making real-time AI a practical part of scientific instrumentation rather than just a benchmark exercise.

Sources:

1. Fast inference of deep neural networks in FPGAs for particle physics — Javier Duarte, Song Han, Philip Harris, Sergo Jindariani, Edward Kreinar, Benjamin Kreis, Jennifer Ngadiuba, Maurizio Pierini, Ryan Rivera, Nhan Tran, Zhenbin Wu, 2018

http://arxiv.org/abs/1804.06913

2. A Survey on Performance Optimization of High-Level Synthesis Tools — Lan Huang, Da-Lin Li, Kang-Ping Wang, Teng Gao, Adriano Tavares, 2020

https://scholar.google.com/scholar?q=A+Survey+on+Performance+Optimization+of+High-Level+Synthesis+Tools

3. FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks — Michaela Blott, Thomas B. Preusser, Nicholas J. Fraser, Giulio Gambardella, Kenneth O'Brien, Yaman Umuroglu, Miriam Leeser, Kees Vissers, 2018

https://scholar.google.com/scholar?q=FINN-R%3A+An+End-to-End+Deep-Learning+Framework+for+Fast+Exploration+of+Quantized+Neural+Networks

4. Fast inference of deep neural networks in FPGAs for particle physics — Javier Duarte, Song Han, Philip Harris, Sergo Jindariani, Edward Kreinar, Jennifer Ngadiuba, Maurizio Pierini, Nhan Tran, Zhenbin Wu, et al., 2018

https://scholar.google.com/scholar?q=Fast+inference+of+deep+neural+networks+in+FPGAs+for+particle+physics

5. Fast convolutional neural networks on FPGAs with hls4ml — Thea Aarrestad, Vladimir Loncar, Nicolo Ghielmetti, Maurizio Pierini, Sioni Summers, Jennifer Ngadiuba, Javier Duarte, Philip Harris, et al., 2021

https://scholar.google.com/scholar?q=Fast+convolutional+neural+networks+on+FPGAs+with+hls4ml

6. FINN: A Framework for Fast, Scalable Binarized Neural Network Inference — Yaman Umuroglu, Nicholas J. Fraser, Giulio Gambardella, Michaela Blott, Philip H. W. Leong, Magnus Jahre, Kees Vissers, 2017

https://scholar.google.com/scholar?q=FINN%3A+A+Framework+for+Fast%2C+Scalable+Binarized+Neural+Network+Inference

7. Serving DNNs in Real Time at Datacenter Scale with Project Brainwave — Eric Chung, Jeremy Fowers, Kalin Ovtcharov, Michael Papamichael, Adrian Caulfield, Todd Massengill, Ming Liu, Doug Burger, et al., 2018

https://scholar.google.com/scholar?q=Serving+DNNs+in+Real+Time+at+Datacenter+Scale+with+Project+Brainwave

8. Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors — Claudionor N. Coelho Jr., Aki Kuusela, Shan Li, Hao Zhuang, Jennifer Ngadiuba, Thea K. Aarrestad, Vladimir Loncar, Maurizio Pierini, et al., 2021

https://scholar.google.com/scholar?q=Automatic+heterogeneous+quantization+of+deep+neural+networks+for+low-latency+inference+on+the+edge+for+particle+detectors

9. Learning both Weights and Connections for Efficient Neural Network — Song Han, Jeff Pool, John Tran, William J. Dally, 2015

https://scholar.google.com/scholar?q=Learning+both+Weights+and+Connections+for+Efficient+Neural+Network

10. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding — Song Han, Huizi Mao, William J. Dally, 2016

https://scholar.google.com/scholar?q=Deep+Compression%3A+Compressing+Deep+Neural+Networks+with+Pruning%2C+Trained+Quantization+and+Huffman+Coding

11. Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning — Andrew J. Larkoski, Ian Moult, Benjamin Nachman, 2020

https://scholar.google.com/scholar?q=Jet+Substructure+at+the+Large+Hadron+Collider%3A+A+Review+of+Recent+Advances+in+Theory+and+Machine+Learning

12. Deep-learning Top Taggers or The End of QCD? — Gregor Kasieczka, Tilman Plehn, Michael Russell, Torben Schell, 2017

https://scholar.google.com/scholar?q=Deep-learning+Top+Taggers+or+The+End+of+QCD%3F

13. From High-Level Deep Neural Models to FPGAs — Hardik Sharma, Jongse Park, Divya Mahajan, Emmanuel Amaro, Joon Kyung Kim, Chenkai Shao, Asit Mishra, Hadi Esmaeilzadeh, 2016

https://scholar.google.com/scholar?q=From+High-Level+Deep+Neural+Models+to+FPGAs

14. Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics — Yutaro Iiyama, Gianluca Cerminara, Abhijay Gupta, Jan Kieseler, Vladimir Loncar, Maurizio Pierini, Shah Rukh Qasim and collaborators, 2021

https://scholar.google.com/scholar?q=Distance-Weighted+Graph+Neural+Networks+on+FPGAs+for+Real-Time+Particle+Reconstruction+in+High+Energy+Physics

15. Low latency transformer inference on FPGAs for physics applications with hls4ml — not confirmed from snippet; likely hls4ml/particle-physics collaboration, recent

https://scholar.google.com/scholar?q=Low+latency+transformer+inference+on+FPGAs+for+physics+applications+with+hls4ml

16. Optimizing transformer models for low-latency inference: techniques, architectures, and code implementations — not confirmed from snippet, recent

https://scholar.google.com/scholar?q=Optimizing+transformer+models+for+low-latency+inference%3A+techniques%2C+architectures%2C+and+code+implementations

17. Low-bit mixed-precision quantization and acceleration of CNN for FPGA deployment — not confirmed from snippet, recent

https://scholar.google.com/scholar?q=Low-bit+mixed-precision+quantization+and+acceleration+of+CNN+for+FPGA+deployment

18. MPQA: Mixed-Precision Quantization Accelerator for CNN Inference — not confirmed from snippet, recent

https://scholar.google.com/scholar?q=MPQA%3A+Mixed-Precision+Quantization+Accelerator+for+CNN+Inference

19. Fine-grained structured sparse computing for FPGA-based AI inference — not confirmed from snippet, recent

https://scholar.google.com/scholar?q=Fine-grained+structured+sparse+computing+for+FPGA-based+AI+inference

20. Efficient CNN inference acceleration on FPGAs: a pattern pruning-driven approach — not confirmed from snippet, recent

https://scholar.google.com/scholar?q=Efficient+CNN+inference+acceleration+on+FPGAs%3A+a+pattern+pruning-driven+approach

21. Online Learning Extreme Learning Machine with Low-Complexity Predictive Plasticity Rule and FPGA Implementation — not confirmed from snippet, recent

https://scholar.google.com/scholar?q=Online+Learning+Extreme+Learning+Machine+with+Low-Complexity+Predictive+Plasticity+Rule+and+FPGA+Implementation

22. An FPGA architecture for online learning using the Tsetlin machine — not confirmed from snippet, recent

https://scholar.google.com/scholar?q=An+FPGA+architecture+for+online+learning+using+the+Tsetlin+machine

23. AI Post Transformers: FPGA Neural Network Accelerators for Space — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-26-fpga-neural-network-accelerators-for-spa-3087ae.mp3

Interactive Visualization: Fast FPGA Inference for LHC Triggers