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26.2 A Neuromorphic Multiplier-Less Bit-Serial Weight-Memory-Optimized 1024-Tree Brain-State Classifier and Neuromodulation SoC with an 8-Channel Noise-Shaping SAR ADC Array by Gerard O'Leary ; Jianxiong Xu ; Liam Long ; Jose Sales Filho ; Camilo Tejeiro ; Maged ElAnsary ; Chenxi Tang ; Homeira Moradi; Prajay Shah; Taufik A. Valiante; Roman Genovl University of Toronto is a nonfiction available to read on EtoBox.

What is 26.2 A Neuromorphic Multiplier-Less Bit-Serial Weight-Memory-Optimized 1024-Tree Brain-State Classifier and Neuromodulation SoC with an 8-Channel Noise-Shaping SAR ADC Array about?

Personalized medical brain implants have the potential to revolutionize the treatment of neurological disorders and augment cognition. Critically, these devices require accurate, energy-efficient brain-state classifiers to determine the precise moment when the treatment neuromodulation efficacy is maximized, such as before the onset of a seizure in epilepsy [1]. The SoC presented in this work addresses this requirement by combining a bank of 8 neural signal ADCs with BrainForest, an accurate, low-power classification core comprised of a 1024-tree exponentially decaying memory decision forest (EDM-DF). Full closed-loop neuromodulation is supported through the responsive actuation of an on-chip electrical neurostimulator.

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Author

Gerard O'Leary ; Jianxiong Xu ; Liam Long ; Jose Sales Filho ; Camilo Tejeiro ; Maged ElAnsary ; Chenxi Tang ; Homeira Moradi; Prajay Shah; Taufik A. Valiante; Roman Genovl University of Toronto

Publisher

IEEE

Published

2020

Language

EN

ISBN

9781728132051

Category

nonfiction

Subjects

Computer Science, Algorithms And Data Structures, Stem