In recent years, many accelerators have been proposed to efficiently process sparse tensor algebra applications (e.g., sparse neural networks). However, these proposals are single points in a large and diverse design space. The lack of systematic description and modeling support for these sparse tensor accelerators impedes hardware designers from efficient and effective design space exploration. This paper first presents a unified taxonomy to systematically describe the diverse sparse tensor accelerator design space. Based on the proposed taxonomy, it then introduces Sparseloop, the first fast, accurate, and flexible analytical modeling framework to enable early-stage evaluation and exploration of sparse tensor accelerators. Sparseloop comprehends a large set of architecture specifications, including various dataflows and sparse acceleration features (e.g., elimination of zero-based compute). Using these specifications, Sparseloop evaluates a design's processing speed and energy efficiency while accounting for data movement and compute incurred by the employed dataflow as well as the savings and overhead introduced by the sparse acceleration features using stochastic tensor density models. Across representative accelerators and workloads, Sparseloop achieves over 2000 times faster modeling speed than cycle-level simulations, maintains relative performance trends, and achieves 0.1% to 8% average error. With a case study, we demonstrate Sparseloop's ability to help reveal important insights for designing sparse tensor accelerators (e.g., it is important to co-design orthogonal design aspects).
翻译:近些年来,提出了许多加速器,以高效处理稀疏的高温代数应用(例如,神经网络稀少),然而,这些建议是大而多样化的设计空间中的单点。缺乏系统描述和模型支持,这些稀疏的高温加速器阻碍了硬件设计者高效和高效设计空间探索。本文件首先提出一个统一的分类学,以系统描述稀疏的高温加速器设计空间。根据拟议的分类学,然后推出Sparseloop,这是第一个快速、准确和灵活的分析模型框架,以便早期评估和探索稀疏的高压加速器。由于缺少系统描述和模型设计能力,因此这些微缓加速器缺乏系统描述和模型支持,包括各种数据流和稀少的加速器特性(例如,消除零基计算空间空间空间探索)。使用这些规格,Sparseloop对设计的速度和能源效率进行评估,同时核算使用数据流进行的数据流动和计算,以及使用精密的加速度重要加速度设计设计器设计设计能力,从而实现高压压速度。Spareal 10-cx 将业绩周期维持为平均速度。Screcial a cle a cloadal cle a cloadal cloadal ex bedal bedal by sal by its