Valencia Daniel, Alimohammad Amir
Department of Electrical and Computer Engineering, San Diego State University, San Diego, USA.
Department of Electrical and Computer Engineering, University of California, La Jolla, USA.
Biomed Eng Lett. 2022 Dec 9;13(1):73-83. doi: 10.1007/s13534-022-00255-7. eCollection 2023 Feb.
While brain-implantable neural spike sorting can be realized using efficient algorithms, the presence of noise may make it difficult to maintain high-peformance sorting using conventional techniques. In this article, we explore the use of partially binarized neural networks (PBNNs), to the best of our knowledge for the first time, for sorting of neural spike feature vectors. It is shown that compared to the waveform template-based methods, PBNNs offer robust spike sorting over various datasets and noise levels. The ASIC implementation of the PBNN-based spike sorting system in a standard 180-nm CMOS process is presented. The post place and route simulations results show that the synthesized PBNN consumes only 0.59 W of power from a 1.8 V supply while operating at 24 kHz and occupies 0.15 mm of silicon area. It is shown that the designed PBNN-based spike sorting system not only offers comparable accuracy to the state-of-the-art spike sorting systems over various noise levels and datasets, it also occupies a smaller silicon area and consumes less power and energy. This makes PBNNs a viable alternative towards the implementation of brain-implantable spike sorting systems.
虽然使用高效算法可以实现可植入大脑的神经尖峰分类，但噪声的存在可能使得使用传统技术难以维持高性能分类。在本文中，据我们所知，我们首次探索使用部分二值化神经网络（PBNN）来对神经尖峰特征向量进行分类。结果表明，与基于波形模板的方法相比，PBNN在各种数据集和噪声水平下都能提供稳健的尖峰分类。本文介绍了基于PBNN的尖峰分类系统在标准180纳米CMOS工艺中的ASIC实现。布局布线后的仿真结果表明，合成的PBNN在24千赫兹运行时，从1.8伏电源获取的功耗仅为0.59瓦，占用的硅面积为0.15平方毫米。结果表明，所设计的基于PBNN的尖峰分类系统不仅在各种噪声水平和数据集上具有与现有最先进尖峰分类系统相当的精度，而且占用的硅面积更小，功耗和能量更低。这使得PBNN成为实现可植入大脑的尖峰分类系统的可行替代方案。
