Multi-diseases detection with memristive system on chip
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910657159364608 |
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| author | Wang, Zihan Yang, Daniel W. Liu, Zerui Yan, Evan Sun, Heming Ge, Ning Hu, Miao Wu, Wei |
| author_facet | Wang, Zihan Yang, Daniel W. Liu, Zerui Yan, Evan Sun, Heming Ge, Ning Hu, Miao Wu, Wei |
| contents | This study presents the first implementation of multilayer neural networks on a memristor/CMOS integrated system on chip (SoC) to simultaneously detect multiple diseases. To overcome limitations in medical data, generative AI techniques are used to enhance the dataset, improving the classifier's robustness and diversity. The system achieves notable performance with low latency, high accuracy (91.82%), and energy efficiency, facilitated by end-to-end execution on a memristor-based SoC with ten 256x256 crossbar arrays and an integrated on-chip processor. This research showcases the transformative potential of memristive in-memory computing hardware in accelerating machine learning applications for medical diagnostics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14882 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-diseases detection with memristive system on chip Wang, Zihan Yang, Daniel W. Liu, Zerui Yan, Evan Sun, Heming Ge, Ning Hu, Miao Wu, Wei Hardware Architecture Signal Processing C.1.3; I.2.0 This study presents the first implementation of multilayer neural networks on a memristor/CMOS integrated system on chip (SoC) to simultaneously detect multiple diseases. To overcome limitations in medical data, generative AI techniques are used to enhance the dataset, improving the classifier's robustness and diversity. The system achieves notable performance with low latency, high accuracy (91.82%), and energy efficiency, facilitated by end-to-end execution on a memristor-based SoC with ten 256x256 crossbar arrays and an integrated on-chip processor. This research showcases the transformative potential of memristive in-memory computing hardware in accelerating machine learning applications for medical diagnostics. |
| title | Multi-diseases detection with memristive system on chip |
| topic | Hardware Architecture Signal Processing C.1.3; I.2.0 |
| url | https://arxiv.org/abs/2410.14882 |