Embedding error correction into crossbars for reliable matrix vector multiplication using emerging devices
Qiuwen Lou, Tianqi Gao, Patrick Faley, Michael Niemier, Xiaobo Sharon Hu, Siddharth Joshi
Abstract
Emerging memory devices are an attractive choice for implementing very energy-efficient in-situ matrix-vector multiplication (MVM) for use in intelligent edge platforms. Despite their great potential, device-level non-idealities have a large impact on the application-level accuracy of deep neural network (DNN) inference. We introduce a low-density parity-check code (LDPC) based approach to correct non-ideality induced errors encountered during in-situ MVM. We first encode the weights using error correcting codes (ECC), perform MVM on the encoded weights, and then decode the result after in-situ MVM. We show that partial encoding of weights can maintain DNN inference accuracy while minimizing the overhead of LDPC decoding. Within two iterations, our ECC method recovers 60% of the accuracy in MVM computations when 5% of underlying computations are error-prone. Compared to an alternative ECC method which uses arithmetic codes, using LDPC improves AlexNet classification accuracy by 0.8% at iso-energy. Similarly, at iso-energy, we demonstrate an improvement in CIFAR-10 classification accuracy of 54% with VGG-11 when compared to a strategy that uses 2× redundancy in weights. Further design space explorations demonstrate that we can leverage the resilience endowed by ECC to improve energy efficiency (by reducing operating voltage). A 3.3× energy efficiency improvement in DNN inference on CIFAR-10 dataset with VGG-11 is achieved at iso-accuracy.