A 0.96pJ/SOP, 30.23K-neuron/mm^2 Heterogeneous Neuromorphic Chip With Fullerene-like Interconnection Topology for Edge-AI Computing

TL;DR

This paper introduces a highly energy-efficient, dense, and scalable neuromorphic chip with a novel fullerene-like interconnection topology, optimized for edge-AI applications, combining multiple technologies for enhanced performance.

Contribution

It presents a new neuromorphic SoC with a fullerene-like NoC topology, achieving high neuron density and low power consumption for edge-AI computing.

Findings

Achieves 0.96 pJ/SOP energy efficiency.

Neuron density of 30.23K neurons/mm^2.

Power density reduced by 67.5% compared to related works.

Abstract

Edge-AI computing requires high energy efficiency, low power consumption, and relatively high flexibility and compact area, challenging the AI-chip design. This work presents a 0.96 pJ/SOP heterogeneous neuromorphic system-on-chip (SoC) with fullerene-like interconnection topology for edge-AI computing. The neuromorphic core integrates different technologies to augment computing energy efficiency, including sparse computing, partial membrane potential updates, and non-uniform weight quantization. Multiple neuromorphic cores and multi-mode routers form a fullerene-like network-on-chip (NoC). The average degree of communication nodes exceeds traditional topologies by 32%, with a minimal degree variance of 0.93, allowing advanced decentralized on-chip communication. Additionally, the NoC can be scaled up through extended off-chip high-level router nodes. A RISC-V CPU and a neuromorphic processor are tightly coupled and fabricated within a 5.42 mm^2 die area under 55 nm CMOS technology. The chip has a low power density of 0.52 mW/mm^2, reducing 67.5% compared to related works, and achieves a high neuron density of 30.23 K/mm^2. Eventually, the chip is demonstrated to be effective on different datasets and achieves 0.96 pJ/SOP energy efficiency.