tinyML Summit 2022: TinyML for All: Full-stack Optimization for Diverse Edge AI Platforms

tinyML Summit 2022
tinyML Vision Session
TinyML for All: Full-stack Optimization for Diverse Edge AI Platforms
Di WU, Co-founder and CEO, OmniML

Today’s AI is too big, as modern deep learning requires a massive amount of computational resources, carbon footprint, and engineering efforts. This makes TinyML extremely difficult because of the limited hardware resource, the power budget, and the deploying challenges. The fundamental cause of this problem is the mismatch between AI models and hardware, and we are solving it from the root by improving the efficiency of a neural network through model compression, neural architecture rebalances, and new design primitives. Our research is highlighted by full-stack optimizations, including the neural network topology, inference library, and hardware architecture, which allows a larger design space to unearth the underlying principles. This enables us to deploy real-world AI applications on tiny microcontroller units (MCUs), despite the limited memory size and compute power. Based on this technology, we also launched a commercial company that helps the industry solve its Edge AI problems.

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