InfiniMind: A Learning-Optimized Large-Scale Brain-Computer Interface

Abstract

Brain-computer interfaces (BCIs) provide an interactive closed-loop connection between the brain and a computer. By employing signal processors implanted within the brain, BCIs are driving innovations across various fields in neuroscience and medicine. Recent studies highlight the need to integrate non-volatile memories (NVMs) into the implanted system for large-scale applications. At the same time, they emphasize the importance of continual learning within the system to address non-stationarities in the recorded signals. This work is the first to address the performance and lifetime issues of deploying learning on NVM-assisted BCI systems. To reduce excessive write overhead associated with learning support, we propose four optimization schemes tailored for BCI workloads. First, update filtering minimizes unnecessary writes by leveraging the sparse and recurring nature of BCI signals. Second, delta buffering exploits temporal locality inherent in BCI signals to minimize NVM writes. Third, out-of-place flushing reduces write amplification by packing multiple sub-page updates into a single page write. Fourth, waveform compression decreases the volume of written data by exploiting the structural characteristics of neural signals.We implement these optimizations in a memory controller and integrate it into the state-of-the-art NVM-assisted BCI system, realizing an endto- end learning-optimized system. Evaluation results show that our system improves performance and lifetime by 5.39× and 23.52×, respectively, on representative continual learning algorithms.