Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search.

ABSTRACT: Lifelong on-device learning is a key challenge for machine intelligence, and this requires learning from few, often single, samples. Memory-augmented neural networks have been proposed to achieve the goal, but the memory module must be stored in off-chip memory, heavily limiting the practical use. In this work, we experimentally validated that all different structures in the memory-augmented neural network can be implemented in a fully integrated memristive crossbar platform with an accuracy that closely matches digital hardware. The successful demonstration is supported by implementing new functions in crossbars, including the crossbar-based content-addressable memory and locality sensitive hashing exploiting the intrinsic stochasticity of memristor devices. Simulations show that such an

SUBMITTER: Mao R 

PROVIDER: S-EPMC9587027 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature