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Title: EDALearn: A Comprehensive RTL-to-Signoff EDA Benchmark for Democratized and Reproducible ML for EDA Research
Award ID(s):
2106828
PAR ID:
10623831
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
ACM
Date Published:
ISBN:
9798400710773
Page Range / eLocation ID:
1 to 8
Format(s):
Medium: X
Location:
Newark Liberty International Airport Marriott New York NY USA
Sponsoring Org:
National Science Foundation
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  1. The advent of heterogeneous integration (HI) places new demands on EDA tooling. Building large systems requires (1) methods for chiplet disaggregation that map the system to smaller chiplets, working in conjunction with system-technology co-optimization to determine the right design decisions that optimize computation and communication, together with the choice of substrate and chiplet technologies; (2) multiphysics and multiscale analyses that incorporate thermomechanical aspects into performance analysis, ranging from fast machine-learning- driven analyses in early stages to signoff-quality multiphysics-based analysis; (3) physical design techniques for placing and routing chiplets and embedded active/passive elements on and within the substrate, including the design of thermal and power delivery solutions; and (4) underlying infrastructure required to facilitate HI-based design, including the design and characterization of chiplet libraries and the establishment of data formats and standards. This paper overviews these issues and lays out a set of EDA needs for HI designs. 
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