Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio Processing on Extreme Edge
The escalating challenges of managing vast sensor-generated data, particularly in audio applications, necessitate innovative solutions. Current systems face significant computational and storage demands, especially in real-time applications like gunshot detection systems (GSDS), and the proliferatio...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10896650/ |
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| author | Sanggeon Yun Ryozo Masukawa Hanning Chen Sungheon Jeong Wenjun Huang Arghavan Rezvani Minhyoung Na Yoshiki Yamaguchi Mohsen Imani |
| author_facet | Sanggeon Yun Ryozo Masukawa Hanning Chen Sungheon Jeong Wenjun Huang Arghavan Rezvani Minhyoung Na Yoshiki Yamaguchi Mohsen Imani |
| author_sort | Sanggeon Yun |
| collection | DOAJ |
| description | The escalating challenges of managing vast sensor-generated data, particularly in audio applications, necessitate innovative solutions. Current systems face significant computational and storage demands, especially in real-time applications like gunshot detection systems (GSDS), and the proliferation of edge sensors exacerbates these issues. This paper proposes a groundbreaking approach with a near-sensor model tailored for intelligent audio-sensing frameworks. Utilizing a Fast Fourier Transform (FFT) module, convolutional neural network (CNN) layers, and HyperDimensional Computing (HDC), our model excels in low-energy, rapid inference, and online learning. It is highly adaptable for efficient ASIC design implementation, offering superior energy efficiency compared to conventional embedded CPUs or GPUs, and is compatible with the trend of shrinking microphone sensor sizes. Comprehensive evaluations at both software and hardware levels underscore the model’s efficacy. Software assessments through detailed ROC curve analysis revealed a delicate balance between energy conservation and quality loss, achieving up to 82.1% energy savings with only 1.39% quality loss. Hardware evaluations highlight the model’s commendable energy efficiency when implemented via ASIC design, especially with the Google Edge TPU, showcasing its superiority over prevalent embedded CPUs and GPUs. |
| format | Article |
| id | doaj-art-3beb311db2814c42bb6aae13b83f8946 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3beb311db2814c42bb6aae13b83f89462025-08-20T03:01:32ZengIEEEIEEE Access2169-35362025-01-0113439474395510.1109/ACCESS.2025.354323210896650Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio Processing on Extreme EdgeSanggeon Yun0https://orcid.org/0000-0002-0488-9666Ryozo Masukawa1https://orcid.org/0009-0009-8988-1388Hanning Chen2https://orcid.org/0000-0003-1956-5135Sungheon Jeong3https://orcid.org/0000-0003-3540-7065Wenjun Huang4https://orcid.org/0009-0005-7416-3587Arghavan Rezvani5https://orcid.org/0000-0002-1012-5785Minhyoung Na6Yoshiki Yamaguchi7https://orcid.org/0009-0001-5485-0399Mohsen Imani8https://orcid.org/0000-0002-5761-0622Department of Computer Science, University of California at Irvine, Irvine, CA, USADepartment of Computer Science, University of California at Irvine, Irvine, CA, USADepartment of Computer Science, University of California at Irvine, Irvine, CA, USADepartment of Computer Science, University of California at Irvine, Irvine, CA, USADepartment of Computer Science, University of California at Irvine, Irvine, CA, USADepartment of Computer Science, University of California at Irvine, Irvine, CA, USADepartment of Computer Science, Kookmin University, Seoul, Republic of KoreaDepartment of Electronic Information Systems, Shibaura Institute of Technology, Saitama, JapanDepartment of Computer Science, University of California at Irvine, Irvine, CA, USAThe escalating challenges of managing vast sensor-generated data, particularly in audio applications, necessitate innovative solutions. Current systems face significant computational and storage demands, especially in real-time applications like gunshot detection systems (GSDS), and the proliferation of edge sensors exacerbates these issues. This paper proposes a groundbreaking approach with a near-sensor model tailored for intelligent audio-sensing frameworks. Utilizing a Fast Fourier Transform (FFT) module, convolutional neural network (CNN) layers, and HyperDimensional Computing (HDC), our model excels in low-energy, rapid inference, and online learning. It is highly adaptable for efficient ASIC design implementation, offering superior energy efficiency compared to conventional embedded CPUs or GPUs, and is compatible with the trend of shrinking microphone sensor sizes. Comprehensive evaluations at both software and hardware levels underscore the model’s efficacy. Software assessments through detailed ROC curve analysis revealed a delicate balance between energy conservation and quality loss, achieving up to 82.1% energy savings with only 1.39% quality loss. Hardware evaluations highlight the model’s commendable energy efficiency when implemented via ASIC design, especially with the Google Edge TPU, showcasing its superiority over prevalent embedded CPUs and GPUs.https://ieeexplore.ieee.org/document/10896650/Audio sensingnear-sensor intelligent sensinghyperdimensional computingASIC design |
| spellingShingle | Sanggeon Yun Ryozo Masukawa Hanning Chen Sungheon Jeong Wenjun Huang Arghavan Rezvani Minhyoung Na Yoshiki Yamaguchi Mohsen Imani Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio Processing on Extreme Edge IEEE Access Audio sensing near-sensor intelligent sensing hyperdimensional computing ASIC design |
| title | Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio Processing on Extreme Edge |
| title_full | Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio Processing on Extreme Edge |
| title_fullStr | Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio Processing on Extreme Edge |
| title_full_unstemmed | Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio Processing on Extreme Edge |
| title_short | Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio Processing on Extreme Edge |
| title_sort | hyperdimensional intelligent sensing for efficient real time audio processing on extreme edge |
| topic | Audio sensing near-sensor intelligent sensing hyperdimensional computing ASIC design |
| url | https://ieeexplore.ieee.org/document/10896650/ |
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