用存内计算芯片实现低功耗事件序列处理的状态空间模型
Compute-in-Memory Implementation of State Space Models for Event Sequence Processing
- 重参数化模型，用实数系数和共享衰减常数降低硬件映射复杂度
- 在基于忆阻器的存内计算阵列上实现状态演化，支持异步事件处理
- 兼顾高精度与高能效，适合视觉与音频的实时事件处理场景
状态空间模型（SSMs）作为长序列处理的新范式，在多个基准测试中超越传统方法，可统一递归与卷积网络，并模拟生物系统关键功能。本文提出一种在低功耗存内计算（CIM）硬件上实现SSMs的方法，支持实时、事件驱动的处理。通过重参数化使模型使用实数系数和共享衰减常数，显著降低实际硬件系统的映射复杂度。利用器件动态特性与对角化状态转移参数，状态演化可在基于交叉阵列的CIM系统中原生实现，结合具有短期记忆特性的忆阻器。该软硬件协同设计实现了高精度与高能效，同时支持事件驱动的视觉与音频任务全异步处理。
原文摘要 · Abstract (English)
State space models (SSMs) have recently emerged as a powerful framework for long sequence processing, outperforming traditional methods on diverse benchmarks. Fundamentally, SSMs can generalize both recurrent and convolutional networks and have been shown to even capture key functions of biological systems. Here we report an approach to implement SSMs in energy-efficient compute-in-memory (CIM) hardware to achieve real-time, event-driven processing. Our work re-parameterizes the model to function with real-valued coefficients and shared decay constants, reducing the complexity of model mapping onto practical hardware systems. By leveraging device dynamics and diagonalized state transition parameters, the state evolution can be natively implemented in crossbar-based CIM systems combined with memristors exhibiting short-term memory effects. Through this algorithm and hardware co-design, we show the proposed system offers both high accuracy and high energy efficiency while supporting fully asynchronous processing for event-based vision and audio tasks.
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