Griffin: Rethinking Sparse Optimization for Deep Learning Architectures
Published: Apr 1, 2022
Abstract
This paper examines the design space trade-offs of DNNs accelerators aiming to achieve competitive performance and efficiency metrics for all four combinations of dense or sparse activation/weight tensors. To do so, we systematically examine the overheads of supporting sparsity on top of an optimized dense core. These overheads are modeled based on parameters that indicate how a multiplier can borrow a nonzero operation from the neighboring...
Paper Details
Title
Griffin: Rethinking Sparse Optimization for Deep Learning Architectures
Published Date
Apr 1, 2022
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