标题： HyperCam：物联网摄像头的低功耗板载计算机视觉 显示英文标题

标题： HyperCam: Low-Power Onboard Computer Vision for IoT Cameras

摘要： 我们介绍了HyperCam，这是一种节能的图像分类管道，能够在低功耗物联网摄像系统上执行计算机视觉任务。HyperCam利用超维计算，在低功耗微控制器上高效地进行训练和推理。我们使用现成的硬件实现了一个低功耗无线摄像平台，并证明HyperCam在MNIST、Fashion-MNIST、人脸检测和人脸识别任务中分别可以达到93.60%、84.06%、92.98%和72.79%的准确率，同时在资源效率方面显著优于其他分类器。具体而言，它在峰值时使用了42.91-63.00KB闪存和22.25KB RAM，推理延迟为0.08-0.27秒。在其他机器学习分类器如SVM、xgBoost、MicroNets、MobileNetV3和MCUNetV3中，HyperCam是唯一一个在保持竞争力的内存占用和推理延迟的同时，达到竞争力的准确率的分类器，满足了低功耗摄像系统的资源需求。 显示英文摘要

摘要： We present HyperCam, an energy-efficient image classification pipeline that enables computer vision tasks onboard low-power IoT camera systems. HyperCam leverages hyperdimensional computing to perform training and inference efficiently on low-power microcontrollers. We implement a low-power wireless camera platform using off-the-shelf hardware and demonstrate that HyperCam can achieve an accuracy of 93.60%, 84.06%, 92.98%, and 72.79% for MNIST, Fashion-MNIST, Face Detection, and Face Identification tasks, respectively, while significantly outperforming other classifiers in resource efficiency. Specifically, it delivers inference latency of 0.08-0.27s while using 42.91-63.00KB flash memory and 22.25KB RAM at peak. Among other machine learning classifiers such as SVM, xgBoost, MicroNets, MobileNetV3, and MCUNetV3, HyperCam is the only classifier that achieves competitive accuracy while maintaining competitive memory footprint and inference latency that meets the resource requirements of low-power camera systems.