论文标题
采用正式的尖峰分类算法和硬件评估的方法
Toward A Formalized Approach for Spike Sorting Algorithms and Hardware Evaluation
论文作者
论文摘要
尖峰排序算法用于将神经元种群的细胞外记录分为单单元尖峰活动。定制的硬件实施尖峰排序算法的开发正在迅速发展。但是，缺乏系统的方法和一组标准化评估标准，无法直接比较软件和硬件实现。在本文中，我们正式化了一组标准化标准和一个题为“细胞外记录的合成模拟（SSOER）”（SSOER）的公开合成数据集，这些数据是通过汇总具有不同信噪比（SNRS）的现有合成数据集来构建的。此外，我们提出了一个基准，用于将来比较，并使用我们的标准评估模拟的电阻性随机访问记忆（RRAM）内存计算（IMC）系统系统，使用离散小波转换（DWT）进行特征提取。我们的系统大约消耗（通过通道）10.72兆瓦，在22nm FDSOI互补的金属氧化物 - 氧化物 - 副导体（CMOS）过程中，面积为0.66mm $^2 $。
Spike sorting algorithms are used to separate extracellular recordings of neuronal populations into single-unit spike activities. The development of customized hardware implementing spike sorting algorithms is burgeoning. However, there is a lack of a systematic approach and a set of standardized evaluation criteria to facilitate direct comparison of both software and hardware implementations. In this paper, we formalize a set of standardized criteria and a publicly available synthetic dataset entitled Synthetic Simulations Of Extracellular Recordings (SSOER), which was constructed by aggregating existing synthetic datasets with varying Signal-To-Noise Ratios (SNRs). Furthermore, we present a benchmark for future comparison, and use our criteria to evaluate a simulated Resistive Random-Access Memory (RRAM) In-Memory Computing (IMC) system using the Discrete Wavelet Transform (DWT) for feature extraction. Our system consumes approximately (per channel) 10.72mW and occupies an area of 0.66mm$^2$ in a 22nm FDSOI Complementary Metal-Oxide-Semiconductor (CMOS) process.
