  Exploiting sparsity in deep neural networks (DNNs) has been a promising area
to meet the growing computation need of modern DNNs. However, in practice,
sparse DNN acceleration still faces a key challenge. To minimize the overhead
of sparse acceleration, hardware designers have proposed structured sparse
hardware support recently, which provides limited flexibility and requires
extra model fine-tuning. Moreover, any sparse model fine-tuned for certain
structured sparse hardware cannot be accelerated by other structured hardware.
To bridge the gap between sparse DNN models and hardware, this paper proposes
tensor approximation via structured decomposition (TASD), which leverages the
distributive property in linear algebra to turn any sparse tensor into a series
of structured sparse tensors. Next, we develop a software framework, TASDER, to
accelerate DNNs by searching layer-wise, high-quality structured decomposition
for both weight and activation tensors so that they can be accelerated by any
systems with structured sparse hardware support. Evaluation results show that,
by exploiting prior structured sparse hardware baselines, our method can
accelerate off-the-shelf dense and sparse DNNs without fine-tuning and improves
energy-delay-product by up to 83% and 74% on average.
