%0 Journal Article
%T Precise and low-power closed-loop neuromodulation through algorithm-integrated circuit co-design.
%A Yang J
%A Zhao S
%A Wang J
%A Lin S
%A Hou Q
%A Sawan M
%J Front Neurosci
%V 18
%N 0
%D 2024
%M 38550560
%F 5.152
%R 10.3389/fnins.2024.1340164
%X 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 closed-loop 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.