Sign up to save your podcasts
Or
Share Aptos: Creating ML models that fit your edge device like a glove
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Aptos: Creating ML models that fit your edge device like a glove
Shipping edge AI shouldn’t feel like a marathon through model zoos, missing ops, and latency ceilings. We lay out a practical path to get from your data and constraints to a hardware-ready model—measured on real boards—without the endless back-and-forth between data science and firmware teams. If you’ve wrestled with quantization loss, unsupported kernels, or picking the “right” NPU, this walkthrough will feel like oxygen.
We start by naming the pain: quick demos that collapse under real device limits, foundation models that fail after export, and feedback loops that burn months. From there, we unpack Aptos, our automation engine that turns edge AI into a data in, model out process. The system explores parameterized architecture recipes and neural architecture search, trains promising candidates, and deploys them to a hardware farm packed with evaluation kits. Every candidate returns hard numbers—latency, per-layer timing, memory, on-device accuracy, and power—so tradeoffs are grounded in measurements, not wishful thinking.
What makes it fast is the learning layer. As Aptos accumulates results, meta models predict runtime, memory fit, and stable hyperparameter ranges before committing compute. That means less time wasted on dead ends and more time converging on models that satisfy your KPIs, whether you care about sub-5 ms inference on an i.MX 8 Plus, battery life in the field, or non-square inputs that match your camera feed. We also fold in research-backed techniques—pruning, quantization, distillation—so you benefit from the latest without chasing papers.
If your team is eyeing a chip migration or evaluating new NPUs, a dropdown swap in Aptos triggers a fresh search tuned to the new hardware, minimizing lock-in and keeping options open. The result is timeline compression: where projects used to take 12–18 months with large teams, we aim to surface strong, deployable candidates in one to two weeks. Subscribe for more deep dives into edge AI deployment, share this episode with your team, and leave a review telling us which device you want to target next.
Send us Fan Mail
Support the show
Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org
View all episodes
By EDGE AI FOUNDATIONShipping edge AI shouldn’t feel like a marathon through model zoos, missing ops, and latency ceilings. We lay out a practical path to get from your data and constraints to a hardware-ready model—measured on real boards—without the endless back-and-forth between data science and firmware teams. If you’ve wrestled with quantization loss, unsupported kernels, or picking the “right” NPU, this walkthrough will feel like oxygen.
We start by naming the pain: quick demos that collapse under real device limits, foundation models that fail after export, and feedback loops that burn months. From there, we unpack Aptos, our automation engine that turns edge AI into a data in, model out process. The system explores parameterized architecture recipes and neural architecture search, trains promising candidates, and deploys them to a hardware farm packed with evaluation kits. Every candidate returns hard numbers—latency, per-layer timing, memory, on-device accuracy, and power—so tradeoffs are grounded in measurements, not wishful thinking.
What makes it fast is the learning layer. As Aptos accumulates results, meta models predict runtime, memory fit, and stable hyperparameter ranges before committing compute. That means less time wasted on dead ends and more time converging on models that satisfy your KPIs, whether you care about sub-5 ms inference on an i.MX 8 Plus, battery life in the field, or non-square inputs that match your camera feed. We also fold in research-backed techniques—pruning, quantization, distillation—so you benefit from the latest without chasing papers.
If your team is eyeing a chip migration or evaluating new NPUs, a dropdown swap in Aptos triggers a fresh search tuned to the new hardware, minimizing lock-in and keeping options open. The result is timeline compression: where projects used to take 12–18 months with large teams, we aim to surface strong, deployable candidates in one to two weeks. Subscribe for more deep dives into edge AI deployment, share this episode with your team, and leave a review telling us which device you want to target next.
Send us Fan Mail
Support the show
Learn more about the EDGE AI FOUNDATION - edgeaifoundation.org