The growing scale of large language models (LLMs) has intensified demands on computation and memory, making efficient inference a key challenge. While sparsity can reduce these costs, existing design space exploration (DSE) frameworks often overlook compression formats, a key factor for leveraging sparsity on accelerators. This paper proposes SnipSnap, a joint compression format and dataflow co-optimization framework for efficient sparse LLM accelerator design. SnipSnap introduces: (1) a hierarchical compression format encoding to expand the design space; (2) an adaptive compression engine for selecting formats under diverse sparsity; and (3) a progressive co-search workflow that jointly optimizes dataflow and compression formats. SnipSnap achieves 18.24% average memory energy savings via format optimization, along with 2248.3$\times$ and 21.0$\times$ speedups over Sparseloop and DiMO-Sparse frameworks, respectively.

翻译：大型语言模型（LLM）规模的不断增长加剧了对计算和内存的需求，使得高效推理成为一个关键挑战。虽然稀疏性可以降低这些成本，但现有的设计空间探索（DSE）框架常常忽视压缩格式，而这是利用加速器上稀疏性的一个关键因素。本文提出SnipSnap，一个用于高效稀疏LLM加速器设计的压缩格式与数据流协同优化框架。SnipSnap引入了：（1）一种层次化压缩格式编码以扩展设计空间；（2）一个用于在不同稀疏度下选择格式的自适应压缩引擎；以及（3）一个协同优化数据流和压缩格式的渐进式协同搜索工作流。通过格式优化，SnipSnap实现了平均18.24%的内存能耗节省，同时在Sparseloop和DiMO-Sparse框架上分别获得了2248.3$\times$和21.0$\times$的加速比。