ElfCore: A 28nm Neural Processor Enabling Dynamic Structured Sparse Training and Online Self-Supervised Learning with Activity-Dependent Weight Update

TL;DR

ElfCore is a 28nm neural processor that enables efficient online self-supervised learning and structured sparse training, significantly reducing power and memory while increasing network capacity for sensory signal processing tasks.

Contribution

ElfCore introduces the first integrated local self-supervised learning engine with dynamic sparse training and activity-dependent weight updates in a 28nm neural processor.

Findings

Up to 16X lower power consumption

3.8X reduction in on-chip memory

5.9X greater network capacity efficiency

Abstract

In this paper, we present ElfCore, a 28nm digital spiking neural network processor tailored for event-driven sensory signal processing. ElfCore is the first to efficiently integrate: (1) a local online self-supervised learning engine that enables multi-layer temporal learning without labeled inputs; (2) a dynamic structured sparse training engine that supports high-accuracy sparse-to-sparse learning; and (3) an activity-dependent sparse weight update mechanism that selectively updates weights based solely on input activity and network dynamics. Demonstrated on tasks including gesture recognition, speech, and biomedical signal processing, ElfCore outperforms state-of-the-art solutions with up to 16X lower power consumption, 3.8X reduced on-chip memory requirements, and 5.9X greater network capacity efficiency.