一句话：提出首个通用稀疏计算处理器扩展，以流式 ISA 将流/稀疏向量作为一等公民，灵活加速稀疏张量与图模式计算。
稀疏数据计算日益重要，但现有稀疏加速器多针对特定算法或应用，难以适应快速演进的算法和软件优化。本文提出 SparseCore，这是一种面向稀疏计算的通用处理器扩展。作者扩展指令集架构（ISA），将流或稀疏向量提升为一等公民，并设计高效体系结构组件支持该流式 ISA；新 ISA 直接在流上操作，兼顾高效数据移动与计算。模拟结果表明，SparseCore 在稀疏张量计算和图模式计算上取得显著加速。
Computation on sparse data is becoming increasingly important for many applications. Recent sparse computation accelerators are designed for specific algorithm/application, making them inflexible with software optimizations. This paper proposes SparseCore, the first general-purpose processor extension for sparse computation that can flexibly accelerate complex code patterns and fast-evolving algorithms. We extend the instruction set architecture (ISA) to make stream or sparse vector first-class citizens, and develop efficient architectural components to support the stream ISA. The novel ISA extension intrinsically operates on streams, realizing both efficient data movement and computation. The simulation results show that SparseCore achieves significant speedups for sparse tensor computation and graph pattern computation.