Brain-Inspired Computing Exploiting Carbon Nanotube FETs and Resistive RAM: Hyperdimensional Computing Case Study

📋 论文概要

该论文通过异构集成碳纳米管场效应晶体管（CNFET）和电阻式RAM（RRAM），利用单片3D集成技术实现了一个端到端的脑启发超维计算系统，并成功应用于语言识别等认知任务，解决了传统CMOS技术在能效和计算密度上的瓶颈。

💡 主要创新点

• 首次实现基于CNFET和RRAM的异构3D集成超维计算系统，克服了传统硅基技术的扩展限制。
• 利用超维计算（HDC）的鲁棒性和容错性，结合新兴纳米器件的低功耗特性，实现了高效的认知计算。
• 展示了端到端的系统级验证，从器件到算法全流程集成为脑启发计算提供了新路径。

🏷 关键词

📄 原文摘要

Jan M. Rabaey2, H.-S. Philip Wong1, Max M. Shulaker3, Subhasish Mitra1 Stanford University, Stanford, CA University of California, Berkeley, Berkeley, CA 3 Massachusetts Institute of Technology, Cambridge, MA 1 2 We demonstrate an end-to-end brain-inspired hyperdimensional (HD) computing nanosystem, effective for cognitive tasks such as language recognition, using heterogeneous integration of multiple emerging nanotechnologies. It uses monolithic 3D integration of carbon nanotube field-effect transistors (CNFETs, an emerging logic technology with significant energy-delay product (EDP) benefit vs. silicon CMOS [1]) and Resistive RAM (RRAM, an emerging memory that promises dense non-volatile and analog storage [2]). Due to their low fabrication temperature (<250°C), CNFETs and RRAM naturally enable monolithic 3D integration with fine-grained and dense vertical connections (exceeding various chip stacking and packaging approaches)

👥 作者与机构

Tony F. Wu1, Haitong Li1, Ping-Chen Huang2, Abbas Rahimi2,