标题： 杰克单元：一种支持多种数据格式的面积和能量高效的乘加（MAC）单元 显示英文标题

标题： Jack Unit: An Area- and Energy-Efficient Multiply-Accumulate (MAC) Unit Supporting Diverse Data Formats

摘要： 在本工作中，我们引入了一个面积和能量高效的乘加（MAC）单元，称为Jack单元，它是一个多用途单元，支持多种数据格式，如整数（INT）、浮点数（FP）和微缩放数据格式（MX）。它通过i）用可精度扩展的进位保存乘法器（CSM）替换FP乘法器中的进位保存乘法器（CSM），ii）在CSM内根据指数差异对尾数进行调整，以及iii）利用二维子字并行性，提供位级灵活性并提高硬件效率。为了评估有效性，我们实现了Jack单元和三个基线MAC单元的版图。此外，我们设计了一个配备我们Jack单元的AI加速器，以与一个支持多种数据格式的最先进AI加速器进行比较。与基线MAC单元相比，所提出的MAC单元占用面积小1.17~2.01倍，功耗低1.05~1.84倍。在五个AI基准测试中，使用我们Jack单元设计的加速器在各种数据格式下比基线的能效提高了1.32~5.41倍。 显示英文摘要

摘要： In this work, we introduce an area- and energy-efficient multiply-accumulate (MAC) unit, named Jack unit, that is a jack-of-all-trades, supporting various data formats such as integer (INT), floating point (FP), and microscaling data format (MX). It provides bit-level flexibility and enhances hardware efficiency by i) replacing the carry-save multiplier (CSM) in the FP multiplier with a precision-scalable CSM, ii) performing the adjustment of significands based on the exponent differences within the CSM, and iii) utilizing 2D sub-word parallelism. To assess effectiveness, we implemented the layout of the Jack unit and three baseline MAC units. Additionally, we designed an AI accelerator equipped with our Jack units to compare with a state-of-the-art AI accelerator supporting various data formats. The proposed MAC unit occupies 1.17~2.01x smaller area and consumes 1.05~1.84x lower power compared to the baseline MAC units. On five AI benchmarks, the accelerator designed with our Jack units improves energy efficiency by 1.32~5.41x over the baseline across various data formats.