用微型神经网络+模拟存储器，实现超低功耗边缘分类。
A Hybrid Edge Classifier: Combining TinyML-Optimised CNN with RRAM-CMOS ACAM for Energy-Efficient Inference
- 前端用优化的tinyML提取特征，后端用RRAM-CMOS模拟存储器匹配模板。
- 单次分类能耗仅97.68纳焦，比原模型降低792倍。
- 适合可穿戴等极端资源受限的边缘设备使用。
近年来，为在本地处理信息而发展的智能边缘计算系统日益增多。许多近传感器机器学习（ML）方法已应用于资源受限的边缘传感系统（如可穿戴设备），以实现精准且低功耗的模板匹配。为应对极端边缘场景，结合传统与新兴技术的混合解决方案开始涌现。针对边缘应用优化的深度神经网络（DNN）与新型计算方式（器件与架构层面）相结合，有望在保持高分类精度的同时，将功耗降至传统方案的极小比例。本文提出一种面向极端边缘近传感器系统的软硬件混合边缘分类器，由两部分组成：(i) 作为前端特征提取器的优化数字tinyML网络；(ii) 作为后端模板匹配系统的RRAM-CMOS模拟内容寻址存储器（ACAM）。该混合系统在准确率与能耗之间取得良好平衡，单次分类能耗分别为前段96.23 nJ、后段1.45 nJ，总计97.68 nJ，相较原教师模型78.06 μJ降低了792倍，证明其在极端边缘场景中的可行性。
原文摘要 · Abstract (English)
In recent years, the development of smart edge computing systems to process information locally is on the rise. Many near-sensor machine learning (ML) approaches have been implemented to introduce accurate and energy efficient template matching operations in resource-constrained edge sensing systems, such as wearables. To introduce novel solutions that can be viable for extreme edge cases, hybrid solutions combining conventional and emerging technologies have started to be proposed. Deep Neural Networks (DNN) optimised for edge application alongside new approaches of computing (both device and architecture -wise) could be a strong candidate in implementing edge ML solutions that aim at competitive accuracy classification while using a fraction of the power of conventional ML solutions. In this work, we are proposing a hybrid software-hardware edge classifier aimed at the extreme edge near-sensor systems. The classifier consists of two parts: (i) an optimised digital tinyML network, working as a front-end feature extractor, and (ii) a back-end RRAM-CMOS analogue content addressable memory (ACAM), working as a final stage template matching system. The combined hybrid system exhibits a competitive trade-off in accuracy versus energy metric with $E_{front-end}$ = $96.23 nJ$ and $E_{back-end}$ = $1.45 nJ$ for each classification operation compared with 78.06$μ$J for the original teacher model, representing a 792-fold reduction, making it a viable solution for extreme edge applications.
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