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- Title
Precise and low-power closed-loop neuromodulation through algorithm-integrated circuit co-design.
- Authors
Jie Yang; Shiqi Zhao; Junzhe Wang; Siyu Lin; Qiming Hou; Sawan, Mohamad
- Abstract
Implantable neuromodulation devices have significantly advanced treatments for neurological disorders such as Parkinson's disease, epilepsy, and depression. Traditional open-loop devices like deep brain stimulation (DBS) and spinal cord stimulators (SCS) often lead to overstimulation and lack adaptive precision, raising safety and side-effect concerns. Next-generation closed-loop systems offer real-time monitoring and on-device diagnostics for responsive stimulation, presenting a significant advancement for treating a range of brain diseases. However, the high false alarm rates of current closed-loop technologies limit their efficacy and increase energy consumption due to unnecessary stimulations. In this study, we introduce an artificial intelligence-integrated circuit co-design that targets these issues and using an online demonstration system for closedloop seizure prediction to showcase its effectiveness. Firstly, two neural network models are obtained with neural-network search and quantization strategies. A binary neural network is optimized for minimal computation with high sensitivity and a convolutional neural network with a false alarm rate as low as 0.1/h for false alarm rejection. Then, a dedicated low-power processor is fabricated in 55 nm technology to implement the two models. With reconfigurable design and event-driven processing feature the resulting application-specific integrated circuit (ASIC) occupies only 5mm2 silicon area and the average power consumption is 142 µW. The proposed solution achieves a significant reduction in both false alarm rates and power consumption when benchmarked against state-of-the-art counterparts.
- Subjects
CONVOLUTIONAL neural networks; APPLICATION-specific integrated circuits; ARTIFICIAL neural networks; DEEP brain stimulation; NEUROMODULATION; NEURAL stimulation; ARTIFICIAL implants
- Publication
Frontiers in Neuroscience, 2024, p1
- ISSN
1662-4548
- Publication type
Article
- DOI
10.3389/fnins.2024.1340164