A Multi-Site Accelerator-Rich Processing Fabric for Scalable Brain-Computer Interfacing

TL;DR

Hull is a distributed, accelerator-rich BCI system that achieves 2-3 orders higher data rates than previous methods while maintaining power and latency constraints, enabling advanced brain research and clinical applications.

Contribution

The paper introduces Hull, a novel distributed BCI system with accelerator-rich nodes that significantly increase data rates while adhering to power and latency constraints.

Findings

Reads biological neurons at 2-3 orders higher data rates

Supports brain-wide behaviors and diseases research

Balances modular design with hardware-software co-design

Abstract

Hull is an accelerator-rich distributed implantable Brain-Computer Interface (BCI) that reads biological neurons at data rates that are 2-3 orders of magnitude higher than the prior state of art, while supporting many neuroscientific applications. Prior approaches have restricted brain interfacing to tens of megabits per second in order to meet two constraints necessary for effective operation and safe long-term implantation -- power dissipation under tens of milliwatts and response latencies in the tens of milliseconds. Hull also adheres to these constraints, but is able to interface with the brain at much higher data rates, thereby enabling, for the first time, BCI-driven research on and clinical treatment of brain-wide behaviors and diseases that require reading and stimulating many brain locations. Central to Hull's power efficiency is its realization as a distributed system of BCI nodes with accelerator-rich compute. Hull balances modular system layering with aggressive cross-layer hardware-software co-design to integrate compute, networking, and storage. The result is a lesson in designing networked distributed systems with hardware accelerators from the ground up.