Work

A Learning-Based Recommender System for Autotuning Design Flows of Industrial High-Performance Processors

Year: 2019

Type: conference-paper

Abstract: Logic synthesis and physical design (LSPD) tools automate complex design tasks previously performed by human designers. One time-consuming task that remains manual is configuring the LSPD flow parameters, which significantly impacts design results. To reduce the parameter-tuning effort, we propose an LSPD parameter recommender system that involves learning a collaborative prediction model through tensor decomposition and regression. Using a model trained with archived data from multiple state-of-the-art 14nm processors, we reduce the exploration cost while achieving comparable design quality. Furthermore, we demonstrate the transfer-learning properties of our approach by showing that this model can be successfully applied for 7nm designs.

Source: Proceedings - ACM IEEE Design Automation Conference

DOI: 10.1145/3316781.3323919

Authors: Jihye Kwon, Matthew M. Ziegler, Luca P. Carloni

Institutions: Columbia University, IBM (United States), IBM Research - Thomas J. Watson Research Center

Language: English

Topic: VLSI and FPGA Design Techniques

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funder: National Science Foundation

Award: SHF: Small: Rethinking CAD for System-Level Design via Interactivity, Learning, and Collaboration