Accelerating Chip Design With Machine Learning
Brucek Khailany, Haoxing Ren, Steve Dai, Saad Godil, Ben Keller, Robert M. Kirby, Alicia Klinefelter, Rangharajan Venkatesan, Yanqing Zhang, Bryan Catanzaro, William J. Dally
Abstract
Recent advancements in machine learning provide an opportunity to transform chip design workflows. We review recent research applying techniques such as deep convolutional neural networks and graph-based neural networks in the areas of automatic design space exploration, power analysis, VLSI physical design, and analog design. We also present a future vision of an AI-assisted automated chip design workflow to aid designer productivity and automate optimization tasks.
Topics & Concepts
Computer scienceWorkflowComputer architectureVery-large-scale integrationElectronic design automationArtificial intelligenceChipIntegrated circuit designMachine learningDeep learningConvolutional neural networkDesign space explorationEmbedded systemSoftware engineeringTelecommunicationsDatabaseVLSI and FPGA Design TechniquesManufacturing Process and OptimizationEmbedded Systems Design Techniques