⚡ 本页包含 AI 生成的分析内容，仅供参考
该论文提出了一款384通道的在线尖峰分类（OSS）集成电路，采用无监督Geo-OSort聚类算法，实现了低复杂度、硬件高效的片上处理，解决了高密度神经记录系统中数据传输带宽和实时闭环应用的瓶颈。
neural-recording devices get denser and generate more data [1], on-chip and online neural-signal processing becomes crucial to reduce the data-transmission power and enable real-time closed-loop applications with minimum latency. Spike sorting (SS) is an important data-reduction technique that allows the classification of extracellularly recorded spikes into clusters representing the neuron sources. SS requires computationally intensive algorithms usually implemented offline in software [2]. However, to enable on-chip and online SS (OSS), a low-complexity and hardwareefficient algorithm is required to achieve minimal area, energy and latency, while maintaining comparable accuracy. Although several multi-channel OSS chips have been
Yingping Chen1,2, Bernardo Tacca1, Yunzhu Chen1, Dwaipayan Biswas1,
Georges Gielen1,3, Francky Catthoor1,3, Marian Verhelst1,3, Carolina Mora Lopez1 imec, Leuven, Belgium Fudan University, Shanghai, China 3 KU Leuven, Leuven, Belgium 1 2 As