ArticleNature communications2026
A hardware-adaptive learning algorithm for superlinear-capacity associative memory on memristor crossbars.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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9 authors.
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Abstract
The human brain recalls complete patterns from partial cues via associative memory, but Hopfield neural networks emulating this process are inefficient on conventional hardware, and prior memristor-based implementations are vulnerable to device defects and have limited capacity, particularly for continuous patterns. We introduce a hardware-adaptive learning algorithm that incorporates experimentally calibrated device constraints during training and validate it on an integrated memristor crossbar compute-in-memory platform. The approach improves defect tolerance and effective capacity, achieving threefold higher capacity than a pseudo-inverse baseline at 50% stuck-at faults. The same framework extends to scalable multilayer architectures supporting binary and continuous-valued patterns, where we observe superlinear capacity scaling on correlated data (∝N
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