Hardware-conscious Software Training for Deep Neural Network Inference Accelerator Chips to Recover Accuracy Degradation due to Hardware Variabilities
Deep neural network (DNN) has been widely applied in various industries. Specialized chips are being discussed for the purpose of achieving lower power consumption with higher throughput. Hardware variations introduced during the process of chip manufacturing are the main reason for affecting the inference accuracies. In this paper, we propose hardware-conscious software training (HCST) method which enables high inference accuracies even under the influence of hardware variations.
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