Topology optimization of random memristors for input-aware dynamic SNN

Machine learning has advanced unprecedentedly, exemplified by GPT-4 and SORA. However, they cannot parallel human brains in efficiency and adaptability due to differences in signal representation, optimization, runtime reconfigurability, and hardware architecture. To address these challenges, we introduce pruning optimization for input-aware dynamic memristive spiking neural network (PRIME). PRIME uses spiking neurons to emulate brain's spiking mechanisms and optimizes the topology of random memristive SNNs inspired by structural plasticity, effectively mitigating memristor programming stochasticity. It also uses the input-aware early-stop policy to reduce latency and leverages memristive in-memory computing to mitigate von Neumann bottleneck. Validated on a 40-nm, 256-K memristor-based macro, PRIME achieves comparable classification accuracy and inception score to software baselines, with energy efficiency improvements of 37.8× and 62.5×. In addition, it reduces computational loads by 77 and 12.5% with minimal performance degradation and demonstrates robustness to stochastic memristor noise. PRIME paves the way for brain-inspired neuromorphic computing.

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Publication Details

Journal
Science Advances
Published
2025-04-16
DOI
https://doi.org/10.1126/sciadv.ads5340
Citations
3
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.19
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article

Topology optimization of random memristors for input-aware dynamic SNN

Dashan Shang, Songqi Wang, Guoqi Li, Shaocong Wang et al.
3 citations
Science Advances
Advanced Memory and Neural Computing
2.19
article

Topology optimization of random memristors for input-aware dynamic SNN

Dashan Shang, Songqi Wang, Guoqi Li, Shaocong Wang, Xiaoshan Wu, Yue Deng, Rui Chen, Tao Wan, Jichang Yang, Zhongrui Wang, Yangu He, Yi Li, Ning Lin, Xiaojuan Qi, Yifei Yu, Yue Zhang, Xinyuan Zhang, Bo Wang
article en
3 citations

Abstract

Machine learning has advanced unprecedentedly, exemplified by GPT-4 and SORA. However, they cannot parallel human brains in efficiency and adaptability due to differences in signal representation, optimization, runtime reconfigurability, and hardware architecture. To address these challenges, we introduce pruning optimization for input-aware dynamic memristive spiking neural network (PRIME). PRIME uses spiking neurons to emulate brain's spiking mechanisms and optimizes the topology of random memristive SNNs inspired by structural plasticity, effectively mitigating memristor programming stochasticity. It also uses the input-aware early-stop policy to reduce latency and leverages memristive in-memory computing to mitigate von Neumann bottleneck. Validated on a 40-nm, 256-K memristor-based macro, PRIME achieves comparable classification accuracy and inception score to software baselines, with energy efficiency improvements of 37.8× and 62.5×. In addition, it reduces computational loads by 77 and 12.5% with minimal performance degradation and demonstrates robustness to stochastic memristor noise. PRIME paves the way for brain-inspired neuromorphic computing.

Science AdvancesVol. 11(16)
Chinese Academy of Sciences (CN), Hong Kong Science and Technology Parks Corporation (HK), Beijing Microelectronics Technology Institute (CN), Institute of Microelectronics (SG), Institute of Automation (CN), Institute of Microelectronics (CN), University of Chinese Academy of Sciences (CN), Beihang University (CN), University of Hong Kong (HK)
Affordable and clean energy
Openalex Percentile: Top 13%
Advanced Memory and Neural Computing
2.19
DOIOpen Access PDFOpenAlex
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