发表机构
Politecnico di Torino; STMicroelectronics（都灵理工大学; 意法半导体）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MAUPITI传感器集成16×16 TMOS阵列与RISC-V微控制器，采用原型NCM分类器，在低内存功耗约束下实现红外传感器设备端学习，准确率与传统分类器相当，延迟开销可忽略，支持在线自适应。
AI 中文摘要
低分辨率红外（IR）阵列传感器是嵌入式系统中实现隐私保护人体感知的有趣方案。本研究介绍了一款集成16×16热MOSFET（TMOS）阵列与扩展了低精度SIMD指令的RISC-V微控制器的智能多像素红外传感器，其可在严格的内存与功耗约束（片上内存<32kB，功耗≈1.5mW）下，完成姿态与手势识别任务的设备端学习与持续自适应。为避免反向传播与回放缓冲区的内存开销，本研究采用基于原型的最近类均值（NCM）分类器：其中简单卷积神经网络（CNN）编码器在离线完成训练与量化，而类原型则以流模式在设备端存储与更新。通过在两个数据集上开展实验，结果表明该方法的准确率与传统分类器相当，且分类与原型更新阶段的延迟开销可忽略（两阶段合计<0.29%），有效实现了感知框架的在线自适应。
英文摘要
Low-resolution infrared (IR) array sensors represent an interesting solution for privacy-preserving human sensing in embedded systems. In this letter, we describe a smart multi-pixel IR sensor integrating a 16$\times$16 thermal MOSFET (TMOS) array and a RISC-V microcontroller extended with low-precision SIMD instructions, capable of on-device learning and continual adaptation for pose and gesture recognition tasks under tight memory and power constraints ($<$32kB on-chip memory, $\approx$1.5mW). To avoid the memory overheads of backpropagation and replay buffers, we adopt a prototype-based Nearest Class Mean (NCM) classifier in which a simple Convolutional Neural Network (CNN) encoder is trained and quantized offline, while class prototypes are stored and updated on the device in streaming mode. With experiments on two datasets, we show that this approach yields accuracy on par with a conventional classifier, with negligible latency overheads in both the classification and the prototype update ($<$0.29% considering both phases), effectively enabling online adaptation of the perception framework.
CommentsAccepted for publication in IEEE Embedded Systems Letters
