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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.09166 |
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| _version_ | 1866916857400786944 |
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| author | Guo, Liyuan Weiße, Annika Zeinolabedin, Seyed Mohammad Ali Schüffny, Franz Marcus Stolba, Marco Ma, Qier Wang, Zhuo Scholze, Stefan Dixius, Andreas Berthel, Marc Partzsch, Johannes Walter, Dennis Ellguth, Georg Höppner, Sebastian George, Richard Mayr, Christian |
| author_facet | Guo, Liyuan Weiße, Annika Zeinolabedin, Seyed Mohammad Ali Schüffny, Franz Marcus Stolba, Marco Ma, Qier Wang, Zhuo Scholze, Stefan Dixius, Andreas Berthel, Marc Partzsch, Johannes Walter, Dennis Ellguth, Georg Höppner, Sebastian George, Richard Mayr, Christian |
| contents | Multi-channel electrophysiology systems for recording of neuronal activity face significant data throughput limitations, hampering real-time, data-informed experiments. These limitations impact both experimental neurobiology research and next-generation neuroprosthetics. We present a novel solution that leverages the high integration density of 22nm FDSOI CMOS technology to address these challenges. The proposed highly integrated programmable System-on-Chip comprises 68-channel 0.41 \textmu W/Ch recording frontends, spike detectors, 16-channel 0.87-4.39 \textmu W/Ch action potential and 8-channel 0.32 \textmu W/Ch local field potential codecs, as well as a MAC-assisted power-efficient processor operating at 25 MHz (5.19 \textmu W/MHz). The system supports on-chip training processes for compression, training and inference for neural spike sorting. The spike sorting achieves an average accuracy of 91.48% or 94.12% depending on the utilized features. The proposed PSoC is optimized for reduced area (9 mm2) and power. On-chip processing and compression capabilities free up the data bottlenecks in data transmission (up to 91% space saving ratio), and moreover enable a fully autonomous yet flexible processor-driven operation. Combined, these design considerations overcome data-bottlenecks by allowing on-chip feature extraction and subsequent compression. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_09166 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 68-Channel Highly-Integrated Neural Signal Processing PSoC with On-Chip Feature Extraction, Compression, and Hardware Accelerators for Neuroprosthetics in 22nm FDSOI Guo, Liyuan Weiße, Annika Zeinolabedin, Seyed Mohammad Ali Schüffny, Franz Marcus Stolba, Marco Ma, Qier Wang, Zhuo Scholze, Stefan Dixius, Andreas Berthel, Marc Partzsch, Johannes Walter, Dennis Ellguth, Georg Höppner, Sebastian George, Richard Mayr, Christian Signal Processing Multi-channel electrophysiology systems for recording of neuronal activity face significant data throughput limitations, hampering real-time, data-informed experiments. These limitations impact both experimental neurobiology research and next-generation neuroprosthetics. We present a novel solution that leverages the high integration density of 22nm FDSOI CMOS technology to address these challenges. The proposed highly integrated programmable System-on-Chip comprises 68-channel 0.41 \textmu W/Ch recording frontends, spike detectors, 16-channel 0.87-4.39 \textmu W/Ch action potential and 8-channel 0.32 \textmu W/Ch local field potential codecs, as well as a MAC-assisted power-efficient processor operating at 25 MHz (5.19 \textmu W/MHz). The system supports on-chip training processes for compression, training and inference for neural spike sorting. The spike sorting achieves an average accuracy of 91.48% or 94.12% depending on the utilized features. The proposed PSoC is optimized for reduced area (9 mm2) and power. On-chip processing and compression capabilities free up the data bottlenecks in data transmission (up to 91% space saving ratio), and moreover enable a fully autonomous yet flexible processor-driven operation. Combined, these design considerations overcome data-bottlenecks by allowing on-chip feature extraction and subsequent compression. |
| title | 68-Channel Highly-Integrated Neural Signal Processing PSoC with On-Chip Feature Extraction, Compression, and Hardware Accelerators for Neuroprosthetics in 22nm FDSOI |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2407.09166 |