arXiv:2606. 10445v1 Announce Type: new Abstract: Semi-structured 2:4 sparsity is widely supported by modern accelerators, providing up to a 2x theoretical speedup.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
Semi-structured 2:4 sparsity is widely supported by modern accelerators, providing up to a 2x theoretical speedup. However, its strict 50% sparsity constraint often causes non-negligible accuracy degradation under post-training pruning.
arXiv:2607. 08786v1 Announce Type: cross Abstract: With the growing deployment of large language models (LLMs), LLM inference cost has become a key challenge.
arXiv:2607. 21985v1 Announce Type: cross Abstract: The increasing deployment of large language models (LLMs) has magnified the computational and memory bottlenecks of autoregressive decoding, where low compute intensity and bandwidth-bound kernels dominate inference cost.
Celty introduces a co-designed sparse format, GPU kernel, and SIMT microarchitecture to efficiently handle Sparse Matrix‑Sparse Vector (SpMSpV) workloads in large language model inference. Its Run‑Length Compressed CSC (RLC‑CSC) format allows vectorized loading of compressed weight columns and skips memory accesses by exploiting both weight pruning and activation sparsity. The Celty Sparse SIMT Core adds a pipelined RLC decoder that eliminates software index reconstruction and uses local registers for conflict‑free accumulation, achieving up to 5.3× speedup over cuBLAS at 70% dual‑sparsity.
arXiv:2608. 01536v1 Announce Type: cross Abstract: Large Language Models (LLMs) increasingly rely on sparsity to reduce inference cost, but most prior work targets a single sparsity source-either weight or activation-and optimizes for batched multi-user inference.
arXiv:2607. 28418v1 Announce Type: cross Abstract: Pruning is a promising approach for improving the efficiency of LLMs.