28nm芯片实现动态稀疏训练与在线自监督学习，能效提升近6倍。
ElfCore: A 28nm Neural Processor Enabling Dynamic Structured Sparse Training and Online Self-Supervised Learning with Activity-Dependent Weight Update
- 芯片内置动态稀疏训练与活动依赖权重更新机制
- 在手势、语音等任务上功耗降低16倍，内存减少3.8倍
- 适合低功耗边缘设备中的实时感知与自适应学习
本文提出ElfCore，一款面向事件驱动感知信号处理的28nm数字脉冲神经网络处理器。它是首个高效集成三项技术的芯片：(1) 局部在线自监督学习引擎，无需标签即可实现多层时序学习；(2) 动态结构化稀疏训练引擎，支持高精度稀疏到稀疏训练；(3) 基于输入活动与网络动态的稀疏权重更新机制，仅根据活跃度选择性更新权重。在手势识别、语音与生物医学信号处理任务中，相比现有方案，功耗降低最高达16倍，片上内存需求减少3.8倍，网络容量效率提升5.9倍。
原文摘要 · Abstract (English)
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 16X lower power consumption, 3.8X reduced on-chip memory requirements, and 5.9X greater network capacity efficiency.
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