Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs

TL;DR

This paper reviews the emerging opportunities and challenges of integrating on-chip AI into next-generation implantable brain-machine interfaces, focusing on technological hurdles and potential design solutions for advanced prosthetic applications.

Contribution

It provides a comprehensive overview of the technological challenges and proposes algorithmic and IC design solutions for AI-enabled high-channel-count BMIs.

Findings

Identification of key technological challenges in on-chip AI for BMIs

Proposed algorithmic solutions for high-channel-count neural processing

Discussion of IC design strategies to enhance BMI performance

Abstract

Neuroscience and neurotechnology are currently being revolutionized by artificial intelligence (AI) and machine learning. AI is widely used to study and interpret neural signals (analytical applications), assist people with disabilities (prosthetic applications), and treat underlying neurological symptoms (therapeutic applications). In this brief, we will review the emerging opportunities of on-chip AI for the next-generation implantable brain-machine interfaces (BMIs), with a focus on state-of-the-art prosthetic BMIs. Major technological challenges for the effectiveness of AI models will be discussed. Finally, we will present algorithmic and IC design solutions to enable a new generation of AI-enhanced and high-channel-count BMIs.