Automating Constraint-Aware Datapath Optimization using E-Graphs
Automating Constraint-Aware Datapath Optimization using E-Graphs
复制标题
使用电子图自动进行约束感知数据路径优化
DOI：
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Theo Drane
中科院分区：
文献类型：
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作者：
Samuel Coward;G. Constantinides;Theo Drane
数值硬件设计需要积极的优化，其中设计者利用分支约束，创建仅在输入空间的子域上有效的优化机会。我们开发了一个RTL优化工具，可以自动学习条件分支的结果，并利用这些知识进行深度优化。该工具部署基于抽象解释理论的定制程序分析，当与称为电子图的数据结构相结合时，简化了对程序属性的复杂推理。我们的工具可以全自动地从计算机算术文献中发现已知的浮点架构，并优于基线EDA工具，生成速度快33%，电路尺寸小41%。
Numerical hardware design requires aggressive optimization, where designers exploit branch constraints, creating optimization opportunities that are valid only on a sub-domain of input space. We developed an RTL optimization tool that automatically learns the consequences of conditional branches and exploits that knowledge to enable deep optimization. The tool deploys custom built program analysis based on abstract interpretation theory, which when combined with a data-structure known as an e-graph simplifies complex reasoning about program properties. Our tool fully-automatically discovers known floating-point architectures from the computer arithmetic literature and out-performs baseline EDA tools, generating up to 33% faster and 41% smaller circuits.
Egg：快速且可扩展的平等饱和
影响因子：
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作者：
Willsey, Max;Nandi, Chandrakana;Wang, Yisu Remy;Flatt, Oliver;Tatlock, Zachary;Panchekha, Pavel
通讯作者：
Panchekha, Pavel