High Throughput Neuromorphic Brain Interface with CuOx Resistive Crossbars for Real-time Spike Sorting

Real-time spike sorting with large data throughput is essential for studying neural dynamics and brain-machine interfaces. Neural recordings from high-density multi-electrode arrays that consist of hundreds of electrodes impose stringent demands on spike sorting hardware regarding data transmission bandwidth and computation complexity. That leads to an urgent need for specialized hardware with high throughput, low power, and latency. Here, we present a real-time spike sorting processor that utilizes high-density BEOL-integrable CuO
x
resistive crossbars to perform in-memory spike segregation. We experimentally demonstrate, for the first time, efficient hardware implementation of spike sorting from in vivo extracellular recordings with high accuracy. Our neuromorphic interface promises substantial performance gains (
$\sim 1000\times \text{less area}, \sim 200\times \text{less power}, 4.8\ \mu\mathrm{s}$
latency for sorting 100 channels) for in vivo real-time spike sorting.