A 256-Channel 0.227µJ/class Versatile Brain Activity Classification and Closed-Loop Neuromodulation SoC with 0.004mm2-1.51µW/channel Fast-Settling Highly Multiplexed Mixed-Signal Front-End

📋 论文概要

本文提出了一款256通道的闭环神经调控SoC，支持脑活动分类与神经调控，每分类能耗仅0.227µJ，解决了现有SoC通道数少（8-32）和通用性差的问题。

💡 主要创新点

• 实现了256通道的高通道数脑机接口SoC，远超现有8-32通道的设计。
• 每分类能耗低至0.227µJ，且每通道面积仅0.004mm²，实现了高能效与高集成度。
• 支持多种脑活动分类与闭环神经调控，具备通用性。

🏷 关键词

📄 原文摘要

Yashwanth Vyza1, Alix Trouillet1, Stéphanie P. Lacour1,3, Mahsa Shoaran1,3 EPFL, Lausanne, Switzerland Cornell University, Ithaca, NY 3 Center for Neuroprosthetics, Geneva, Switzerland 1 2 Closed-loop neuromodulation can alleviate disease symptoms and provide sensory feedback in various neurological disorders and injuries [1]. Energy-efficient realization of closed-loop devices with on-site classification is critical to enhancing therapeutic efficacy. Despite recent advances, existing SoCs with integrated machine learning are constrained by low channel count (8-32) [2-5] and poor generalizability. To address these limitations, this paper presents a versatile neuromodulation SoC that integrates: (1) a 256-channel area-efficient dynamically addressable analog front-end (AFE), (2) information-rich multi-symptom biomarkers, (3) a low-power tree-structured hierarchical neural network (NeuralTree) classifier, and (4) a 16-channel high-voltage (HV) compliant

👥 作者与机构

Uisub Shin1,2, Laxmeesha Somappa1, Cong Ding1, Bingzhao Zhu1,2,