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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한의용생체공학회 Biomedical Engineering Letters (BMEL) Biomedical Engineering Letters (BMEL) Vol.12 No.2
- 발행연도
- 2022.5
- 수록면
- 185 - 195 (11page)
- DOI
- https://doi.org/10.1007/s13534-022-00217-z
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Conventional spike sorting and motor intention decoding algorithms are mostly implemented on an external computingdevice, such as a personal computer. The innovation of high-resolution and high-density electrodes to record the brain’sactivity at the single neuron level may eliminate the need for spike sorting altogether while potentially enabling in vivo neuraldecoding. This article explores the feasibility and efficient realization of in vivo decoding, with and without spike sorting. Theefficiency of neural network-based models for reliable motor decoding is presented and the performance of candidate neuraldecoding schemes on sorted single-unit activity and unsorted multi-unit activity are evaluated. A programmable processorwith a custom instruction set architecture, for the first time to the best of our knowledge, is designed and implemented forexecuting neural network operations in a standard 180-nm CMOS process. The processor’s layout is estimated to occupy49 mm2 of silicon area and to dissipate 12 mW of power from a 1.8 V supply, which is within the tissue-safe operation ofthe brain.
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