SSR: Sparse Segment Reduction for Ternary GEMM Acceleration
Large Language Models (LLMs) require substantial computational resources, limiting their deployment on resource-constrained hardware. Ternary LLMs mitigate these demands through weight quantization via ternary values, achieving significant compression often with 50-90% sparsity. However, existing approaches have limitations: methods optimized for ternary weights, such as BitNet, redundant segment reduction (RSR), and its improved version RSR++, do not exploit sparsity structures, while conventional sparse formats neglect ternary characteristics, foregoing dual optimization opportunities. In this paper, we introduce Sparse Segment Reduction (SSR), a ternary matrix multiplication method des...
| Authors | Adeline Pittet, Shien Zhu, Valérie Verdan et al. |
|---|---|
| Primary category | cs.LG |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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