  Machine/deep-learning (ML/DL) based techniques are emerging as a driving
force behind many cutting-edge technologies, achieving high accuracy on
computer vision workloads such as image classification and object detection.
However, training these models involving large parameters is both
time-consuming and energy-hogging. In this regard, several prior works have
advocated for sparsity to speed up the of DL training and more so, the
inference phase. This work begins with the observation that during training,
sparsity in the forward and backward passes are correlated. In that context, we
investigate two types of sparsity (input and output type) inherent in gradient
descent-based optimization algorithms and propose a hardware micro-architecture
to leverage the same. Our experimental results use five state-of-the-art CNN
models on the Imagenet dataset, and show back propagation speedups in the range
of 1.69$\times$ to 5.43$\times$, compared to the dense baseline execution. By
exploiting sparsity in both the forward and backward passes, speedup
improvements range from 1.68$\times$ to 3.30$\times$ over the sparsity-agnostic
baseline execution. Our work also achieves significant reduction in training
iteration time over several previously proposed dense as well as sparse
accelerator based platforms, in addition to achieving order of magnitude energy
efficiency improvements over GPU based execution.
