Introducing HyperSense, our co-designed hardware and software system efficiently controls Analog-to-Digital Converter (ADC) modules' data generation rate based on object presence predictions in sensor data. Addressing challenges posed by escalating sensor quantities and data rates, HyperSense reduces redundant digital data using energy-efficient low-precision ADC, diminishing machine learning system costs. Leveraging neurally-inspired HyperDimensional Computing (HDC), HyperSense analyzes real-time raw low-precision sensor data, offering advantages in handling noise, memory-centricity, and real-time learning. Our proposed HyperSense model combines high-performance software for object detection with real-time hardware prediction, introducing the novel concept of Intelligent Sensor Control. Comprehensive software and hardware evaluations demonstrate our solution's superior performance, evidenced by the highest Area Under the Curve (AUC) and sharpest Receiver Operating Characteristic (ROC) curve among lightweight models. Hardware-wise, our FPGA-based domain-specific accelerator tailored for HyperSense achieves a 5.6x speedup compared to YOLOv4 on NVIDIA Jetson Orin while showing up to 92.1% energy saving compared to the conventional system. These results underscore HyperSense's effectiveness and efficiency, positioning it as a promising solution for intelligent sensing and real-time data processing across diverse applications.

翻译：本文提出HyperSense，一种软硬件协同设计的系统，能够根据传感器数据中的物体存在预测，高效控制模数转换器（ADC）模块的数据生成速率。针对传感器数量与数据速率不断攀升带来的挑战，HyperSense利用高能效低精度ADC减少冗余数字数据，从而降低机器学习系统成本。该系统基于神经启发的高维计算（HDC）技术，可实时分析原始低精度传感器数据，并在噪声处理、以内存为中心的设计以及实时学习方面具有优势。我们提出的HyperSense模型将用于物体检测的高性能软件与实时硬件预测相结合，引入了智能传感器控制这一新颖概念。全面的软硬件评估表明，我们的解决方案在轻量级模型中取得了最高的曲线下面积（AUC）和最陡峭的接收者操作特征（ROC）曲线，展现出卓越性能。在硬件层面，我们为HyperSense定制的基于FPGA的领域专用加速器，相较于NVIDIA Jetson Orin上的YOLOv4实现了5.6倍的加速，同时相比传统系统最高可节省92.1%的能耗。这些结果充分证明了HyperSense的有效性与高效性，使其成为跨领域智能感知与实时数据处理中极具前景的解决方案。