Energy Efficient Convolutional Neural Networks for EEG Artifact Detection
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Public Domain Mark 1.0
This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Abstract
This paper proposes an energy e cient Convolutional
Neural Network based architecture for detecting different
types of artifacts in multi-channel EEG signals. Our method
achieves an average artifact detection accuracy of 74% and
precision of 92% across seven different artifact types which
outperforms existing techniques in terms of classification accuracy
as well as the more common ICA based solution in
terms of computational complexity and memory requirements.
We designed a minimal neural network processor whose Verilog
HDL is configurable for implementing 2ⁿ processing engines
(PEs). We deployed our CNN on the processor, placed and routed
on Artix-7 FPGA and examined different number of PEs at
different operating frequencies. Our experiments indicate that
utilizing 4 PEs operating at a clock frequency of 11.1 MHz is
the optimal configuration for our hardware to yield the least
classification energy consumption of 32 mJ accomplished in the
maximum allowed prediction time of 1 Sec. We also implemented
our CNN on TX2 NVIDIA Jetson platform and, by tweaking the
CPU and the GPU frequencies, explored the minimum power and
energy configuration. Our FPGA results indicate that the 4-PE
implementation outperforms the low power configuration of TX2
by 65x in terms of power, and the low energy configuration of
TX2 by 2x in terms of energy per classification. Our CNN-based
FPGA implementation method also outperforms the ICA method
by 11x in terms of energy consumption per classification.