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Found 6 papers, 2 papers with code
The Sparsity Roofline: Understanding the Hardware Limits of Sparse Neural Networks
We achieve this through a novel analytical model for predicting sparse network performance, and validate the predicted speedup using several real-world computer vision architectures pruned across a range of sparsity patterns and degrees.
Understanding the Effect of the Long Tail on Neural Network Compression
E. g., it has been shown that mismatches between the full and compressed models can be biased towards under-represented classes.
HighLight: Efficient and Flexible DNN Acceleration with Hierarchical Structured Sparsity
Due to complex interactions among various deep neural network (DNN) optimization techniques, modern DNNs can have weights and activations that are dense or sparse with diverse sparsity degrees.
Efficient Sparsely Activated Transformers
Transformer-based neural networks have achieved state-of-the-art task performance in a number of machine learning domains including natural language processing and computer vision.
Going Beyond Classification Accuracy Metrics in Model Compression
With the rise in edge-computing devices, there has been an increasing demand to deploy energy and resource-efficient models.
A Programmable Approach to Neural Network Compression
Deep neural networks (DNNs) frequently contain far more weights, represented at a higher precision, than are required for the specific task which they are trained to perform.
