TFix: Exploiting the Natural Redundancy of Ternary Neural Networks for Fault Tolerant In-Memory Vector Matrix Multiplication

In-memory computing (IMC) and quantization have emerged as promising techniques for edge-based deep neural network (DNN) accelerators by reducing their energy, latency and storage requirements. In pursuit of ultra-low precision, ternary precision DNNs (TDNNs) offer high efficiency without sacrificing much inference accuracy. In this work, we explore the impact of hard faults on IMC based TDNNs and propose TFix to enhance their fault tolerance. TFix exploits the natural redundancy present in most ternary IMC bitcells as well as the high weight sparsity in TDNNs to provide up to 40.68% accuracy increase over the baseline with < 6% energy overhead.