This course teaches how to design power-efficient hardware for AI using bio-inspiration as a guide. The learning outcomes include understanding the challenges of energy dissipation in data-centric applications like AI, exploring semiconductor technology solutions, and leveraging spiking neural networks for increased energy efficiency. The course covers topics such as in-memory computing, memory requirements, bio-inspired hardware design, and the use of Non-Volatile Memories. The intended audience for this course includes researchers, engineers, and individuals interested in embedded AI and hardware design for AI applications.
TinyML Talks France - How to Design a Power Frugal Hardware for AI - The Bio-Inspiration Path
Overview
Syllabus
Introduction
Welcome
Start
AI at the edge
Trends in computing
Inmemory compute
Nonwater memory
Memory requirements
Future applications
Bioinspired
Proof of concept
topology
classical domain
receptive fields
resistive rams
analog nuance
fabrication
classification accuracy
demo
scaling
multiple values
viability
unit topologies
multicore implementation
conclusion
embedded RAM
tinyML resources
Mass testing
Compression
Other labs
Image classification
Bioinspired path
Depth analysis
Unsupervised learning
Rules for unsupervised learning
Flexibility
Research
Sponsors
Taught by
tinyML
