A Neuromorphic Brain Interface Based on RRAM Crossbar Arrays for High Throughput Real-Time Spike Sorting
| Autor: | Yuhan Shi, Akshay Ananthakrishnan, Sangheon Oh, Xin Liu, Gopabandhu Hota, Gert Cauwenberghs, Duygu Kuzum |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
template matching
Spike Sorting nonvolatile memory High Throughput Crossbar Neurosciences real-time RRAM Article Electronic Optical and Magnetic Materials memory Non-volatile memory Affordable and Clean Energy resistive random-access memory Copper Oxide Electrical and Electronic Engineering Applied Physics |
| Zdroj: | IEEE Trans Electron Devices IEEE transactions on electron devices, vol 69, iss 4 |
| ISSN: | 1557-9646 0018-9383 |
| DOI: | 10.1109/ted.2021.3131116 |
| Popis: | Real-time spike sorting and processing are crucial for closed-loop brain-machine interfaces and neural prosthetics. Recent developments in high-density multi-electrode arrays with hundreds of electrodes have enabled simultaneous recordings of spikes from a large number of neurons. However, the high channel count imposes stringent demands on real-time spike sorting hardware regarding data transmission bandwidth and computation complexity. Thus, it is necessary to develop a specialized real-time hardware that can sort neural spikes on the fly with high throughputs while consuming minimal power. Here, we present a real-time, low latency spike sorting processor that utilizes high-density CuO(x) resistive crossbars to implement in-memory spike sorting in a massively parallel manner. We developed a fabrication process which is compatible with CMOS BEOL integration. We extensively characterized switching characteristics and statistical variations of the CuO(x) memory devices. In order to implement spike sorting with crossbar arrays, we developed a template matching-based spike sorting algorithm that can be directly mapped onto RRAM crossbars. By using synthetic and in vivo recordings of extracellular spikes, we experimentally demonstrated energy efficient spike sorting with high accuracy. Our neuromorphic interface offers substantial improvements in area (~1000× less area), power (~200× less power), and latency (4.8μs latency for sorting 100 channels) for real-time spike sorting compared to other hardware implementations based on FPGAs and microcontrollers. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|