Fault-tolerant Neuromorphic Computing with Functional ATPG for Post-manufacturing Re-calibration

Abstract

Neuromorphic fabric based on emerging resistive non-volatile memories (NVM), such as Resistive Random Access Memory (ReRAM), Phase Change Memories (PCM) and Spin Transfer Torque (STT) is a promising approach for efficient hardware implementation of Neural Networks (NNs) due to their low power consumption and latency. However, NVMs suffers from manufacturing process variations and manufacturing defects resulting in a shift in the distribution of NN activations and can lead to degradation of inference accuracy. The shifted distribution can be tracked and re-calibrated by re-calculating the statistics of the batch normalization layer of NN. However, such re-calibration and re-calculation have high overhead in terms of memory, power, and latency. In this paper, we proposed a low overhead post-manufacturing calibration of NVM-based neuromorphic fabric by approximating batch normalization to reduce re-calibration overhead. The proposed method requires only 0.2% of training data as re-calibration input and can regain inference accuracy by up to 72.32% on the MNIST, Fashion-MNIST, and CIFAR-10 benchmark datasets.