Shaeri, MohammadAliAfzal, ArshiaShoaran, Mahsa2022-11-072022-11-072022-11-072022-01-0110.1109/AICAS54282.2022.9870008https://infoscience.epfl.ch/handle/20.500.14299/191921WOS:000859273200049Neuroscience 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.Computer Science, Artificial IntelligenceComputer Science, Hardware & ArchitectureEngineering, Electrical & ElectronicComputer ScienceEngineeringartificial intelligence (ai)machine learning (ml)brain machine interface (bmi)hardware efficiencyprocessortext::conference output::conference proceedings::conference paper