A Configurable Digital Neuromorphic Hardware Generator for Heterogeneous Computing

Abstract

Recent trends in machine learning (ML) have placed a strong emphasis on power- and resource-efficient neural networks, as well as the development of neural networks on edge devices. Spiking neural net-works (SNNs), due to their event-based nature, are one of the most promising types of neural networks for low-power applications. To accelerate and ease the deployment of SNNs on edge devices, this thesis presents a configurable digital neuromorphic hardware generator for heterogeneous computing that is capable of generating resource-efficient SNN processing cores. The proposed hardware generator is de-veloped using SpinalHDL, a high-level hardware description language (HDL), which provides a high level of flexibility in hardware generation. Our generator supports the configuration on various parameters and is capable of generating a tree-structured multi-core architecture of heterogeneous cores. The generator is deployed in a sensor-fusion hand-gesture classification use case, for which the configurability of our hardware generator is a key enabler.