Logic Synthesis From Incomplete Specifications Using Disjoint Support Decomposition

Andrea Costamagna, G. Micheli
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引用次数: 1

Abstract

Approximate logic synthesis is an emerging field that tolerates errors in the synthesized logic circuits for better optimization quality. Indeed, in many computing problems, the requirement of preserving the exact functionality either results in unnecessary overuse of resources or is hardly possible to meet. The latter case is typical of incompletely specified synthesis problems, targeting the hardware implementation of a Boolean function from a partial knowledge of its care set. The missing elements of the care set are named don’t knows. Previous works identified information theory-based decomposition strategies as powerful synthesis tools. Nonetheless, the definition of an automatic method for approximate synthesis is an open problem, and the approximate counterpart of many logic synthesis techniques is still missing. In this paper, we extend a disjoint support decomposition algorithm to target Boolean functions in the presence of don’t knows. Furthermore, we integrate the decomposition in an information theory-based synthesis flow. Relative experiments on the IWLS2020 benchmarks show that, on average, the addition of the designed decomposition to the flow reduces by 15.81% the number of gates and by 9.66% the depth.
基于不相交支持分解的不完全规范逻辑综合
近似逻辑综合是一个新兴的领域,它可以容忍合成逻辑电路中的误差,以获得更好的优化质量。实际上,在许多计算问题中,保持精确功能的要求要么导致不必要的过度使用资源,要么几乎无法满足。后一种情况是典型的不完全指定的综合问题,目标是通过对其关心集的部分知识实现布尔函数的硬件实现。缺失的元素被命名为不知道。以前的工作将基于信息理论的分解策略确定为强大的合成工具。然而,近似综合的自动方法的定义是一个开放的问题,许多逻辑综合技术的近似对应仍然缺失。本文将不相交支持分解算法推广到不知道存在情况下的布尔函数。此外,我们将分解集成到一个基于信息论的合成流程中。在IWLS2020基准上的相关实验表明,在流量中加入设计的分解后,闸门数量平均减少了15.81%,深度平均减少了9.66%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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