
Sign up to save your podcasts
Or
Today we’re joined by Jilei Hou, a VP of Engineering at Qualcomm Technologies. In our conversation with Jilei, we focus on the emergence of generative AI, and how they've worked towards providing these models for use on edge devices. We explore how the distribution of models on devices can help amortize large models' costs while improving reliability and performance and the challenges of running machine learning workloads on devices, including model size and inference latency. Finally, Jilei we explore how these emerging technologies fit into the existing AI Model Efficiency Toolkit (AIMET) framework.
The complete show notes for this episode can be found at twimlai.com/go/633
4.7
412412 ratings
Today we’re joined by Jilei Hou, a VP of Engineering at Qualcomm Technologies. In our conversation with Jilei, we focus on the emergence of generative AI, and how they've worked towards providing these models for use on edge devices. We explore how the distribution of models on devices can help amortize large models' costs while improving reliability and performance and the challenges of running machine learning workloads on devices, including model size and inference latency. Finally, Jilei we explore how these emerging technologies fit into the existing AI Model Efficiency Toolkit (AIMET) framework.
The complete show notes for this episode can be found at twimlai.com/go/633
161 Listeners
470 Listeners
296 Listeners
324 Listeners
143 Listeners
190 Listeners
282 Listeners
87 Listeners
101 Listeners
125 Listeners
190 Listeners
63 Listeners
422 Listeners
33 Listeners
36 Listeners