HyperSense: Hyperdimensional Intelligent Sensing for Energy-Efficient Sparse Data Processing
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916459812225024 |
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| author | Yun, Sanggeon Chen, Hanning Masukawa, Ryozo Barkam, Hamza Errahmouni Ding, Andrew Huang, Wenjun Rezvani, Arghavan Angizi, Shaahin Imani, Mohsen |
| author_facet | Yun, Sanggeon Chen, Hanning Masukawa, Ryozo Barkam, Hamza Errahmouni Ding, Andrew Huang, Wenjun Rezvani, Arghavan Angizi, Shaahin Imani, Mohsen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10267 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HyperSense: Hyperdimensional Intelligent Sensing for Energy-Efficient Sparse Data Processing Yun, Sanggeon Chen, Hanning Masukawa, Ryozo Barkam, Hamza Errahmouni Ding, Andrew Huang, Wenjun Rezvani, Arghavan Angizi, Shaahin Imani, Mohsen Hardware Architecture Artificial Intelligence 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. |
| title | HyperSense: Hyperdimensional Intelligent Sensing for Energy-Efficient Sparse Data Processing |
| topic | Hardware Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2401.10267 |