MVM
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Fast Entropy Decoding for Sparse MVM on GPUs
arXiv:2603.01915v2 Announce Type: replace Abstract: We present a novel, practical approach to speed up sparse matrix-vector multiplication (SpMVM) on GPUs. The novel key idea is to apply lossless entropy coding to further compress the sparse matrix when stored in one of the commonly supported formats. Our method is based on dtANS, our new lossless compression method that improves the entropy coding technique of asymmetric numeral systems (ANS) specifically for fast parallel GPU decoding when...
CRAM-ER: Error-Resilient Spintronic Computational Random Access Memory for Scalable In-Memory Computation
arXiv:2606.02781v1 Announce Type: new Abstract: Deep neural networks (DNNs) have achieved state-of-the-art performance across diverse domains. However, typical Von Neumann compute paradigms face severe memory bottlenecks. Emerging near-memory and compute-in-memory approaches alleviate this but incur significant peripheral overhead.
In-Memory Computing Enabled Deep MIMO Detection to Support Ultra-Low-Latency Communications
arXiv:2508.17820v2 Announce Type: replace Abstract: The development of sixth-generation (6G) mobile networks imposes unprecedented latency and reliability demands on multiple-input multiple-output (MIMO) communication systems, a key enabler of high-speed radio access. Recently, deep unfolding-based detectors, which map iterative algorithms onto neural network architectures, have emerged as a promising approach, combining the strengths of model-driven and data-driven methods to achieve high...