Event-Based Spatially Zooming Neural Interface IC with 10nW/Input Reconfigurable-Inverter Fabric and Input-Adaptive Quantization

📋 论文概要

本文提出了一种基于事件驱动的空间缩放神经接口集成电路，采用可重构逆变器结构和输入自适应量化技术，实现了每个输入通道仅10nW的超低功耗，适用于大规模神经电极阵列，解决了神经接口系统功耗过高的问题。

💡 主要创新点

• 提出事件驱动空间缩放机制，仅在神经活动发生时激活相关通道，大幅降低平均功耗。
• 采用可重构逆变器结构，支持灵活配置以适配不同输入信号特性。
• 引入输入自适应量化技术，根据信号动态范围自动调整量化精度，提升能效。

🏷 关键词

📄 原文摘要

Yu Huang1, Junyu Ma1, Chae Lim1, Lingyun Xu1, Shucheng Gong1, Weian Deng1, Qiaosong Deng1, Jin Che1, Sudip Nag1, Joshua Olorocisimo1, Rhianna Singh1, Yanze Wang1, Jose Sales Filho1, Mandana Mohaved2, Homeira Moradi2, George Eleftheriades1, Taufik Valiante2,3, Roman Genov1 University of Toronto, Toronto, Canada Krembil Neuroscience Center, Toronto, Canada 3 Toronto Western Hospital, Toronto, Canada 1 2 Large-scale neural interface ICs have tens of thousands of electrodes [1-3], enabling a wide range of applications including neural prostheses and therapeutic neuromodulation. However, the human brain contains 86 billion neurons and new frontiers in brain interfacing, such as understanding memory and cognition, will benefit from concurrent access to a million or more of implanted electrodes [4]. Modern microfabrication technologies, including silicon wafer thinning [1], allow for dense co-integration of electrodes and

👥 作者与机构

Jianxiong Xu1, Mustafa Kanchwala1, Mohammad Abdolrazzaghi1, Hanfeng Cai1,