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Computer Science > Artificial Intelligence

arXiv:2204.02362 (cs)
[Submitted on 4 Apr 2022 (v1), last revised 13 Apr 2022 (this version, v2)]

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

Authors:MohammadAli Shaeri, Arshia Afzal, Mahsa Shoaran
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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.
Subjects: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Machine Learning (cs.LG); Signal Processing (eess.SP)
Cite as: arXiv:2204.02362 [cs.AI]
  (or arXiv:2204.02362v2 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2204.02362

Submission history

From: MohammadAli Shaeri [view email]
[v1] Mon, 4 Apr 2022 12:47:07 UTC (1,783 KB)
[v2] Wed, 13 Apr 2022 12:02:18 UTC (1,774 KB)
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