How to Architect Models to Work Well With Hardware

In episode 67 of The Gradient Podcast, Daniel Bashir speaks to Daniel Situnayake.

Daniel is head of Machine Learning at Edge Impulse. He is co-author of the O’Reilly books "AI at the Edge" and "TinyML". Previously, he’s worked on the Tensorflow Lite team at Google AI and co-founded Tiny Farms, an insect farming company. Daniel has also lectured in AIDC technologies at Birmingham City University.

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Outline:

* (00:00) Intro

* (1:40) Daniel S Origin Story: computer networking, RFID/barcoding, earlier jobs, Tiny Farms, Tensorflow Lite, writing on TinyML, and Edge Impulse

* (15:30) Edge AI and questions of embodiment/intelligence in AI

* (21:00) The role of hardware, other constraints in edge AI

* (25:00) Definitions of intelligence

* (29:45) What is edge AI?

* (37:30) The spectrum of edge devices

* (43:45) Innovations in edge AI (architecture, frameworks/toolchains, quantization)

* (53:45) Model compression tradeoffs in edge

* (1:00:30) Federated learning and challenges

* (1:09:00) Intro to Edge Impulse

* (1:20:30) Feature engineering for edge systems, fairness considerations

* (1:25:50) Edge AI and axes in AI (large/small, ethereal/embodied)

* (1:37:00) Daniel and Daniel go off the rails on panpsychism

* (1:54:20) Daniel’s advice for aspiring AI practitioners

* (1:57:20) Outro

Links:

* Daniel’s Twitter and blog

* Edge Impulse