Automating constraint-aware datapath optimization using e-graphs
File(s)
Accepted version
Author(s)
Coward, Samuel
Constantinides, George A
Drane, Theo
Type
Conference Paper
Abstract
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.
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.
Date Issued
2023-09-15
Date Acceptance
2023-03-01
Citation
2023 60th ACM/IEEE Design Automation Conference (DAC), 2023, pp.1-6
Publisher
IEEE
Start Page
1
End Page
6
Journal / Book Title
2023 60th ACM/IEEE Design Automation Conference (DAC)
Copyright Statement
Copyright © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
http://arxiv.org/abs/2303.01839v1
Source
Design Automation Conference
Subjects
cs.AR
cs.AR
Publication Status
Published
Start Date
2023-07-09
Finish Date
2023-07-13
Coverage Spatial
San Francisco, CA, USA