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

By Zhe Su and Giacomo Indiveri
Institute of Neuroinformatics University of Zurich and ETH Zurich

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 16× lower power consumption, 3.8× reduced on-chip memory requirements, and 5.9× greater network capacity efficiency.

Index Terms—self-supervised learning; dynamic structured sparse training; sparse weight update

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