A 16-Channel Fully Configurable Neural SoC With 1.52 μW/Ch Signal Acquisition, 2.79 μW/Ch Real-Time Spike Classifier, and 1.79 TOPS/W Deep Neural Network Accelerator in 22 nm FDSOI
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
With the advent of high-density micro-electrodes arrays, developing neural probes satisfying the real-time and stringent power-efficiency requirements becomes more challenging. A smart neural probe is an essential device in future neuroscientific research and medical applications. To realize such devices, we present a 22 nm FDSOI SoC with complex on-chip real-time data processing and training for neural signal analysis. It consists of a digitally-assisted 16-channel analog front-end with 1.52 $\mu$W/Ch, dedicated bio-processing accelerators for spike detection and classification with 2.79 $\mu$W/Ch, and a 125 MHz RISC-V CPU, utilizing adaptive body biasing at 0.5 V with a supporting 1.79 TOPS/W MAC array. The proposed SoC shows a proof-of-concept of how to realize a high-level integration of various on-chip accelerators to satisfy the neural probe requirements for modern applications.
Details
Original language | English |
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Pages (from-to) | 94-107 |
Number of pages | 14 |
Journal | IEEE transactions on biomedical circuits and systems |
Volume | 16 |
Issue number | 1 |
Publication status | Published - 1 Feb 2022 |
Peer-reviewed | Yes |
External IDs
Scopus | 85123362261 |
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PubMed | 35025750 |
Keywords
Sustainable Development Goals
ASJC Scopus subject areas
Keywords
- accelerator architectures, Biomedical electronics, biomedical signal processing, digital integrated circuits, energy efficiency, implantable devices, neural recording system, spike sorting, unsupervised learning