REACT: a heterogeneous reconfigurable neural network accelerator with software-configurable NoCs for training and inference on wearables

Mohit Upadhyay, Rohan Juneja, Bo Wang, Jun Zhou, Weng-Fai Wong, Li-Shiuan Peh

Abstract: On-chip training improves model accuracy on personalised user data and preserves privacy. This work proposes REACT, an AI accelerator for wearables that has heterogeneous cores supporting both training and inference. REACT's architecture is NoC-centric, with weights, features and gradients distributed across cores, accessed and computed efficiently through software-configurable NoCs. Unlike conventional dynamic NoCs, REACT's NoCs have no buffer queues, flow control or routing, as they are entirely configured by software for each neural network. REACT's online learning realises upto 75% accuracy improvement, and is upto 25× faster and 520× more energy-efficient than state-of-the-art accelerators with similar memory and computation footprint.