Cao, T., Zhang, Z., Goh, W. L., Liu, C., Zhu, Y., & Gao, Y. (2023, October 19). ECG Classification using Binary CNN on RRAM Crossbar with Nonidealities-Aware Training, Readout Compensation and CWT Preprocessing. 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS). https://doi.org/10.1109/biocas58349.2023.10389002
Abstract:
This paper presents an electrocardiogram (ECG)
signal classification method using binary CNN implemented on
RRAM crossbar arrays. A new RRAM crossbar structure is
proposed to provide input-dependent references for adaptive
readout quantization. Hence, binary weights can be represented
with just one RRAM cell, instead of the conventional differential
cell, leading to the reduction of crossbar size by half.
Furthermore, the impacts of crossbar nonidealities is mitigated
with nonidealities-aware training and in-situ readout
compensation. On the other hand, bandpass filter (BPF) based
continuous wavelet transform (CWT) approximation is applied
for ECG signal preprocessing to enhance the feature extraction.
Implemented on 64×64 binary RRAM crossbar arrays, the
proposed binary CNN achieved 98.9% classification sensitivity
on 10-class MIT-BIH ECG datasets, with only 0.7% sensitivity
drop compared to the software version with FP32 weights.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Nanosystems at the Edge programme
Grant Reference no. : A18A1b0055
This research / project is supported by the A*STAR - Cyber-Physiochemical Interface programme
Grant Reference no. : A18A1b0045