TinyML Talks France - How to Design a Power Frugal Hardware for AI - The Bio-Inspiration Path
Explore the bio-inspired approach to designing power-efficient hardware for AI in this tinyML Talks France presentation. Delve into the challenges of embedded AI, focusing on energy dissipation and data movement. Learn about current hardware accelerator designs, weight quantization techniques, and activation sparsity. Compare state-of-the-art technology with biological efficiency, and discover how brain-inspired circuits and technologies like Non-Volatile Memories can enhance data locality. Examine the potential of spiking neural networks and their implementation using novel technologies. Gain insights into inmemory compute, future applications, and proof-of-concept designs. Discuss classification accuracy, scaling possibilities, and multicore implementations. Conclude with an overview of embedded RAM, tinyML resources, and research directions in compression, image classification, and unsupervised learning for power-frugal AI hardware design.