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A Novel Approach To Compressing Sparse Data Tensors
[CTSTA'23] The Sparse Abstract Machine
Native Sparse Attention, Fully Explained: Sparse by Design, 11× Faster | 20-Min Deep Dive
Sparseloop: An Analytical Approach to Sparse Tensor Accelerator Modeling @ MICRO 2022
[Sparse'25] Sparse Computing Drives Energy-Efficient Computation for Artificial Intelligence
Q-Sparse: All Large Language Models can be Fully Sparsely-Activated
Sparse block encoding with low subnormalisation for quantum linear solvers on unstructured meshes
Accelerate Image Classification Tasks With Sparsity and the DeepSparse Runtime
[SPCL_Bcast] High-Performance Sparse Tensor Operations in HiParTI Library
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Last Updated: October 25, 2026
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Hyperreal Specifications for Continuous Sparse Data Computations Principles of Deep Representation Learning | HKU DATA8014 Lecture 8: White-box Transformers for Representation Learning via ... Optimizations and abstractions for Saman Amarasinghe, a revered MIT professor in EECS, leads CSAIL's Commit compiler group. A driving force in compiler ... By the end of this you could reason about — and roughly re-implement — NSA: the three-branch attention that skips most of the ... Project & Seminar, ETH Zürich, Fall 2022 Programming Heterogeneous Computing Systems with GPUs and other Accelerators ... Project website (w/ code): sparseloop.mit.edu/ Paper: sparseloop.mit.edu/documents/2022-micro-sparseloop.pdf ... Encoding Unstructured Meshes for Quantum Solvers Topics: Quantum Physics Article: Learn how to deploy fast and accurate image classification models on commodity CPUs at GPU speeds and better. Get started ... Speaker: Jiajia Li Venue: SPCL_Bcast, recorded on 25 February, 2021 Abstract: This talk will present the recent development of ...
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