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系统——一种软硬件协同设计的方案，通过传感器数据中物体存在性预测来高效控制模数转换器模块的数据生成速率。针对传感器数量和数据速率持续攀升带来的挑战，HyperSense采用低精度ADC降低冗余数字数据量，从而减少机器学习系统的能耗。受神经启发的超维度计算被用于分析实时原始低精度传感器数据，其在噪声处理、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“智能传感器控制”概念。全面的软硬件评估表明，本方案在轻量级模型中取得最优表现，具体体现为最高的AUC值和最陡峭的ROC曲线。硬件实现方面，我们为HyperSense定制的FPGA领域专用加速器相比NVIDIA Jetson Orin上的YOLOv4实现了5.6倍加速，同时比传统系统节省高达92.1%的能耗。这些结果充分证明了HyperSense的有效性与能效优势，使其成为面向多样化应用的智能传感与实时数据处理领域极具前景的解决方案。