tinyML Talks Local Manu Rastogi: Tutorial on micro-kernel based hardware acceleration

tinyML Talks local Webcast - recorded August 13, 2020
"Tutorial on micro-kernel based hardware acceleration"
Manu Rastogi

Energy and compute are both scarce for deep learning deployment at the edge. Rapid innovation in new layer types and network topologies makes it even more challenging. There is also increased pressure on hardware designs and toolchain development for automated and efficient model deployment. Often the hardware and toolchains lag behind in the support of new layers. Since deep learning is becoming more ubiquitous there is stiff competition amongst different hardware vendors to provide the most energy-efficient solutions. The key piece to model deployment at the edge is the mico-kernels or the micro-code that orchestrate the data movement and the computation of these networks on hardware. As part of this talk, we will walk through the matrix multiplication micro-code. We will understand the various trade-offs between different optimization strategies and extend these principles to neural networks.