Open AccessEfficient and Robust Spike-Driven Deep Convolutional Neural Networks Based on NOR Flash Computing Array
Author(s) -
Yachen Xiang,
Peng Huang,
Runze Han,
Chu Li,
Kunliang Wang,
Xiaoyan Liu,
Jinfeng Kang
Publication year - 2020
Publication title -
ieee transactions on electron devices
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.828
H-Index - 186
eISSN - 1557-9646
pISSN - 0018-9383
DOI - 10.1109/ted.2020.2987439
Subject(s) - components, circuits, devices and systems , engineered materials, dielectrics and plasmas
In this article, we propose an efficient and robust spike-driven convolutional neural network (SCNN) based on the NOR flash computing array (NFCA), which is mapped by the pretrained convolutional neural network with the same structure. The spike-driven system eliminates the additional analog-to-digital/digital-to-analog (AD/DA) conversion in the NFCA-based CNN. To study the performance of the hardware implementation, an NFCA-based SCNN for the recognition of the Mixed National Institute of Standards and Technology (MNIST) data set is simulated. Simulation results illustrate that the system achieves 97.94% accuracy with the computing speed of $1 \times 10^{6}$ frame per second (fps). Compared with the typical mixed-signal NFCA-based CNN, the NFCA-based SCNN saves 97% area and 56% energy consumption. Moreover, the NFCA-based SCNN demonstrates great robustness to 30% image noise with less than 2% accuracy loss. The impact of random telegraph noise (RTN) is also greatly reduced in which less than 1% accuracy decrease can be achieved at the 32-nm technology node.