Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs
| Autor: | Shaeri, MohammadAli, Afzal, Arshia, Shoaran, Mahsa |
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| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning Computer Science - Artificial Intelligence machine learning (ml) artificial intelligence (ai) brain machine interface (bmi) GeneralLiterature_MISCELLANEOUS hardware efficiency Machine Learning (cs.LG) Artificial Intelligence (cs.AI) ComputingMethodologies_PATTERNRECOGNITION Hardware Architecture (cs.AR) FOS: Electrical engineering electronic engineering information engineering Electrical Engineering and Systems Science - Signal Processing processor Computer Science - Hardware Architecture |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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