A 1024-Channel 0.00029mm2/ch 74nW/ch Online Spatial Spike-Sorting Chip with Event-Driven Spike Detection and Self-Organizing Map Clustering

📋 论文概要

本文提出了一款1024通道的在线空间尖峰分类芯片，通过事件驱动尖峰检测和自组织映射算法，实现了极小的面积（0.00029mm²/通道）和极低的功耗（74nW/通道），解决了高密度多电极阵列中大量原始数据的片上压缩问题，以支持无线脑机接口操作。

💡 主要创新点

• 采用事件驱动尖峰检测机制，仅在检测到尖峰时激活处理，显著降低功耗。
• 引入自组织映射算法实现片上在线空间尖峰分类，无需离线训练，提升实时性和可扩展性。
• 实现了每通道仅0.00029mm²的面积和74nW的功耗，在1024通道规模下达到极高的集成度和能效。

🏷 关键词

📄 原文摘要

Next-generation brain-computer interfaces will enable motor and speech decoding in humans [1-3] and improve our understanding of brain function [4]. To achieve this requires high-density multi-electrode arrays (HD-MEA) [5,6]. This leads to massive amounts of raw data that must be reduced on-chip to enable wireless operation [7]. Spike sorting (SS) assigns spikes to putative neurons and can reduce the data rate substantially because only the neuron ID needs to be transmitted when a spike occurs. Prior art focuses on improving the scalability and power efficiency of on-chip SS [8-15]. However, they either require a large input buffer [12-14], use temporal features (TF) that do not scale well to multi-channel systems [8-13], access the entire clustering memory for every spike [11-13], or use high

👥 作者与机构

Arash Akhoundi1, Yawende Landbrug1, Pumiao Yan2, E. J. Chichilnisky2,

Boris Murmann3, Dante Gabriel Muratore1 Delft University of Technology, Delft, The Netherlands Stanford University, Stanford, CA 3 University of Hawaii, Honolulu, HI 1 2