Developing Synthesis Flows Without Human Knowledge
This addresses the problem of automating complex design flow development for integrated circuit engineers, representing a novel method rather than an incremental improvement.
The paper tackles the challenge of developing IP-specific synthesis flows for IC/SoC design by presenting a fully autonomous framework that uses Convolutional Neural Networks to generate design-specific synthesis flows without human guidance, successfully demonstrating it on three large-scale designs.
Design flows are the explicit combinations of design transformations, primarily involved in synthesis, placement and routing processes, to accomplish the design of Integrated Circuits (ICs) and System-on-Chip (SoC). Mostly, the flows are developed based on the knowledge of the experts. However, due to the large search space of design flows and the increasing design complexity, developing Intellectual Property (IP)-specific synthesis flows providing high Quality of Result (QoR) is extremely challenging. This work presents a fully autonomous framework that artificially produces design-specific synthesis flows without human guidance and baseline flows, using Convolutional Neural Network (CNN). The demonstrations are made by successfully designing logic synthesis flows of three large scaled designs.