A 42pJ/Decision 3.12TOPS/W Robust In-Memory Machine Learning Classifier with On-Chip Training

📋 论文概要

这篇论文提出了一种基于内存计算的机器学习分类器，支持片上训练，实现了42pJ/决策的低能耗和3.12TOPS/W的高能效，解决了嵌入式传感系统在能量受限下的持续推断和自适应问题。

💡 主要创新点

• 提出了一种鲁棒的内存计算架构，通过将计算嵌入存储阵列中，大幅降低数据移动能耗。
• 首次在内存计算分类器中集成片上训练功能，使其能够适应变化的统计和环境条件。
• 实现了42pJ/decision的超低能量消耗和3.12TOPS/W的高能效比，显著优于传统数字架构。

🏷 关键词

📄 原文摘要

Embedded sensory systems (Fig. 31.2.1) continuously acquire and process data for inference and decision-making purposes under stringent energy constraints. These always-ON systems need to track changing data statistics and environmental conditions, such as temperature, with minimal energy consumption. Digital inference architectures [1,2] are not well-suited for such energy-constrained sensory systems due to their high energy consumption, which is dominated (>75%) by the energy cost of memory read accesses and digital computations. In-memory architectures [3,4] significantly reduce the energy cost by embedding pitch-matched analog computations in the periphery of the SRAM bitcell array (BCA). However, their analog nature combined with stringent area constraints makes these architectures susceptible to process, voltage, and temperature (PVT) variation. Previously, off-chip training [4] has been shown to

👥 作者与机构

Sujan Kumar Gonugondla, Mingu Kang, Naresh Shanbhag

University of Illinois at Urbana-Champaign, IL