STCO: Enhancing Training Efficiency via Structured Sparse Tensor Compilation Optimization
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- STCO: Enhancing Training Efficiency via Structured Sparse Tensor Compilation Optimization
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TSTC: Enabling Efficient Training via Structured Sparse Tensor Compilation
ASPDAC '24: Proceedings of the 29th Asia and South Pacific Design Automation ConferenceNetwork sparsification is an effective technique for Deep Neural Network (DNN) inference acceleration. However, existing sparsification solutions often rely on structured sparsity, which has limited benefits. This is because many sparse storage formats ...
On recovery of sparse signals via l1 minimization
This paper considers constrained l1 minimization methods in a unified framework for the recovery of high-dimensional sparse signals in three settings: noiseless, bounded error, and Gaussian noise. Both l1 minimization with an l∞ constraint (Dantzig ...
Low-rank sparse fully-connected tensor network for tensor completion
AbstractFully-connected tensor network (FCTN) has recently drawn lots of attention in tensor completion due to its full description of all correlations between any two modes. However, the FCTN model has multiple ranks, and existing methods often ignore ...
Highlights- The existing FCTN method often has overfitting problem caused by rank-selection.
- FCTN can be expressed by a coefficient sum of basic tensors.
- Sparsity and low-rank constrain on FCTN’s factors can improve the rank-robustness.
- A ...
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- National Natural Science Foundation of China
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