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Today, NVIDIA is releasing TensorRT version 8.0, which introduces support for the Sparse Tensor Cores available on the NVIDIA Ampere Architecture GPUs. In this post, we discuss how the NVIDIA Ampere Architecture addresses these challenges. It may not work due to differences in the network, task, optimizer, or any hyperparameter. The trouble comes when you try to apply Sparsity X to network B. It has been shown that network A can achieve Sparsity X. Workflow-Much of the current research in network pruning serves as useful existence proofs.This limits the potential performance benefit. Alternate pruning methods that attempt to make acceleration easier, such as coarse-grained pruning that removes blocks of weights, channels, or entire layers, can run into accuracy trouble even sooner. Accuracy-To achieve a useful speedup with fine-grained, unstructured sparsity, the network must be made sparse, which often causes accuracy loss.Standard sparse formats are inefficient for all but high sparsities. Acceleration-Fine-grained, unstructured, weight sparsity lacks structure and cannot use the vector and matrix instructions available in efficient hardware to accelerate common network operations.
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There have long been three challenges to realizing the promised gains. The benefits of sparsity only seem straightforward. If there are zeros in the network, then you don’t need to store or operate on them. Sparsity is one optimization technique that holds the promise of meeting these goals. A more efficient network can make better predictions in a limited time budget, react more quickly to unexpected input, or fit into constrained deployment environments. When deploying a neural network, it’s useful to think about how the network could be made to run faster or take less space. This post was updated Jto reflect NVIDIA TensorRT 8.0 updates.