Title: Deep Neural Network Optimized to Resistive Memory with Nonlinear Current-Voltage Characteristics,
Abstract: Artificial Neural Network computation relies on intensive vector-matrix
multiplications. Recently, the emerging nonvolatile memory (NVM) crossbar array
showed a feasibility of implementing such operations with high energy
efficiency, thus there are many works on efficiently utilizing emerging NVM
crossbar array as analog vector-matrix multiplier. However, its nonlinear I-V
characteristics restrain critical design parameters, such as the read voltage
and weight range, resulting in substantial accuracy loss. In this paper,
instead of optimizing hardware parameters to a given neural network, we propose
a methodology of reconstructing a neural network itself optimized to resistive
memory crossbar arrays. To verify the validity of the proposed method, we
simulated various neural network with MNIST and CIFAR-10 dataset using two
different specific Resistive Random Access Memory (RRAM) model. Simulation
results show that our proposed neural network produces significantly higher
inference accuracies than conventional neural network when the synapse devices
have nonlinear I-V characteristics.