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：我们提出HyperSense——一种软硬件协同设计系统，该系统通过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。为应对传感器数量与数据速率持续攀升带来的挑战，HyperSense采用低精度ADC降低冗余数字数据，从而减少机器学习系统的成本。受神经科学启发的超维度计算（HDC）使HyperSense能够分析实时原始低精度传感器数据，在噪声处理、内存中心化计算和实时学习方面具有显著优势。我们提出的HyperSense模型将高性能物体检测软件与实时硬件预测相结合，首次提出了智能传感器控制这一创新概念。全面的软件与硬件评估表明，该方案具有卓越性能，在轻量级模型中实现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。在硬件实现方面，基于FPGA的HyperSense专用领域加速器相比NVIDIA Jetson Orin平台上的YOLOv4实现了5.6倍加速，同时相较传统系统节能高达92.1%。这些结果充分验证了HyperSense的有效性与高效性，使其成为面向智能感知与实时数据处理的跨领域应用解决方案。