TY - GEN
T1 - XNOR-RRAM
T2 - 2018 Design, Automation and Test in Europe Conference and Exhibition, DATE 2018
AU - Sun, Xiaoyu
AU - Yin, Shihui
AU - Peng, Xiaochen
AU - Liu, Rui
AU - Seo, Jae-sun
AU - Yu, Shimeng
N1 - Publisher Copyright:
© 2018 EDAA.
PY - 2018/4/19
Y1 - 2018/4/19
N2 - Recent advances in deep learning have shown that Binary Neural Networks (BNNs) are capable of providing a satisfying accuracy on various image datasets with significant reduction in computation and memory cost. With both weights and activations binarized to +1 or -1 in BNNs, the high-precision multiply-and-accumulate (MAC) operations can be replaced by XNOR and bit-counting operations. In this work, we propose a RRAM synaptic architecture (XNOR-RRAM) with a bit-cell design of complementary word lines that implements equivalent XNOR and bit-counting operation in a parallel fashion. For large-scale matrices in fully connected layers or when the convolution kernels are unrolled in multiple channels, the array partition is necessary. Multi-level sense amplifiers (MLSAs) are employed as the intermediate interface for accumulating partial weighted sum. However, a low bit-level MLSA and intrinsic offset of MLSA may degrade the classification accuracy. We investigate the impact of sensing offsets on classification accuracy and analyze various design options with different sub-array sizes and sensing bit-levels. Experimental results with RRAM models and 65nm CMOS PDK show that the system with 128×128 sub-array size and 3-bit MLSA can achieve accuracies of 98.43% for MLP on MNIST and 86.08% for CNN on CIFAR-10, showing 0.34% and 2.39% degradation respectively compared to the accuracies of ideal BNN algorithms. The projected energy-efficiency of XNOR-RRAM is 141.18 TOPS/W, showing ∼33X improvement compared to the conventional RRAM synaptic architecture with sequential row-by-row read-out.
AB - Recent advances in deep learning have shown that Binary Neural Networks (BNNs) are capable of providing a satisfying accuracy on various image datasets with significant reduction in computation and memory cost. With both weights and activations binarized to +1 or -1 in BNNs, the high-precision multiply-and-accumulate (MAC) operations can be replaced by XNOR and bit-counting operations. In this work, we propose a RRAM synaptic architecture (XNOR-RRAM) with a bit-cell design of complementary word lines that implements equivalent XNOR and bit-counting operation in a parallel fashion. For large-scale matrices in fully connected layers or when the convolution kernels are unrolled in multiple channels, the array partition is necessary. Multi-level sense amplifiers (MLSAs) are employed as the intermediate interface for accumulating partial weighted sum. However, a low bit-level MLSA and intrinsic offset of MLSA may degrade the classification accuracy. We investigate the impact of sensing offsets on classification accuracy and analyze various design options with different sub-array sizes and sensing bit-levels. Experimental results with RRAM models and 65nm CMOS PDK show that the system with 128×128 sub-array size and 3-bit MLSA can achieve accuracies of 98.43% for MLP on MNIST and 86.08% for CNN on CIFAR-10, showing 0.34% and 2.39% degradation respectively compared to the accuracies of ideal BNN algorithms. The projected energy-efficiency of XNOR-RRAM is 141.18 TOPS/W, showing ∼33X improvement compared to the conventional RRAM synaptic architecture with sequential row-by-row read-out.
UR - http://www.scopus.com/inward/record.url?scp=85042001226&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85042001226&partnerID=8YFLogxK
U2 - 10.23919/DATE.2018.8342235
DO - 10.23919/DATE.2018.8342235
M3 - Conference contribution
AN - SCOPUS:85042001226
T3 - Proceedings of the 2018 Design, Automation and Test in Europe Conference and Exhibition, DATE 2018
SP - 1423
EP - 1428
BT - Proceedings of the 2018 Design, Automation and Test in Europe Conference and Exhibition, DATE 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 19 March 2018 through 23 March 2018
ER -