Probabilistic metaplasticity for continual learning with memristors in spiking networks.

ABSTRACT: Edge devices operating in dynamic environments critically need the ability to continually learn without catastrophic forgetting. The strict resource constraints in these devices pose a major challenge to achieve this, as continual learning entails memory and computational overhead. Crossbar architectures using memristor devices offer energy efficiency through compute-in-memory and hold promise to address this issue. However, memristors often exhibit low precision and high variability in conductance modulation, rendering them unsuitable for continual learning solutions that require precise modulation of weight magnitude for consolidation. Current approaches fall short to address this challenge directly and rely on auxiliary high-precision memory, leading to frequent memory access, high memo

SUBMITTER: Zohora FT 

PROVIDER: S-EPMC11603065 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature