Abstract
Introducing HyperSense, the 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, HyperSense analyzes real-time raw low-precision sensor data, offering advantages in handling noise, memory-centricity, and real-time learning. The 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 the solution's superior performance, evidenced by the highest area under the curve and sharpest receiver operating characteristic curve among lightweight models. Hardware-wise, the field programmable gate array-based domain-specific accelerator tailored for HyperSense achieves a 5.6× 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.
Original language | English (US) |
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Article number | 2400228 |
Journal | Advanced Intelligent Systems |
Volume | 6 |
Issue number | 12 |
DOIs | |
State | Published - Dec 2024 |
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Human-Computer Interaction
- Mechanical Engineering
- Control and Systems Engineering
- Electrical and Electronic Engineering
- Materials Science (miscellaneous)
Keywords
- artificial intelligent
- energy efficiency
- hyperdimensional computing
- intelligent sensing