A Neuromorphic Multiplier-Less Bit-Serial WeightMemory-Optimized 1024-Tree Brain-State Classifier and Neuromodulation SoC with an 8-Channel Noise-Shaping SAR ADC Array

📋 论文概要

该论文提出了一种用于个性化脑植入设备的神经形态无乘法器位串行权重存储器优化的1024树脑状态分类器与神经调节SoC，旨在实现高能效的脑状态实时分类以优化神经调控时机，解决癫痫发作前精准干预的问题。

💡 主要创新点

• 采用无乘法器位串行架构，显著降低硬件复杂度和功耗，适合植入式应用。
• 权重存储器优化设计，结合1024树分类器结构，在保证分类精度的同时减少存储开销。
• 集成了脑状态分类与神经调节功能的单芯片系统，实现闭环神经调控。

🏷 关键词

📄 原文摘要

Camilo Tejeiro1, Maged ElAnsary1, Chenxi Tang1, Homeira Moradi2, Prajay Shah1, Taufik A. Valiante3, Roman Genov1 University of Toronto, Toronto, Canada Krembil Neuroscience Center, Toronto, ON, Canada 3 Toronto Western Hospital, Toronto, Canada 1 2 Personalized medical brain implants have the potential to revolutionize the treatment of neurological disorders and augment cognition. Critically, these devices require accurate, energy-efficient brain-state classifiers to determine the precise moment when the treatment neuromodulation efficacy is maximized, such as before the onset of a seizure in epilepsy [1]. The SoC presented in this work addresses this requirement by combining a bank of 8 neural signal ADCs with BrainForest, an accurate, low-power classification core comprised of a 1024-tree exponentially decaying memory decision forest (EDM-DF). Full closed-loop neuromodulation is supported through the responsive actuation of an on-chip

👥 作者与机构

Gerard O'Leary1, Jianxiong Xu1, Liam Long1, Jose Sales Filho1,