Developing Synthesis Flows Without Human Knowledge

04/16/2018
by   Cunxi Yu, et al.
0

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.

READ FULL TEXT
research
11/14/2018

Performance Estimation of Synthesis Flows cross Technologies using LSTMs and Transfer Learning

Due to the increasing complexity of Integrated Circuits (ICs) and System...
research
10/11/2020

ASSURE: RTL Locking Against an Untrusted Foundry

Semiconductor design companies are integrating proprietary intellectual ...
research
01/20/2022

Hybrid Graph Models for Logic Optimization via Spatio-Temporal Information

Despite the stride made by machine learning (ML) based performance model...
research
10/02/2018

DATC RDF: An Open Design Flow from Logic Synthesis to Detailed Routing

In this paper, we present DATC Robust Design Flow (RDF) from logic synth...
research
05/20/2021

On the Optimization of Behavioral Logic Locking for High-Level Synthesis

The globalization of the electronics supply chain is requiring effective...
research
02/28/2022

Towards Machine Learning for Placement and Routing in Chip Design: a Methodological Overview

Placement and routing are two indispensable and challenging (NP-hard) ta...
research
08/25/2020

Residual Network Based Direct Synthesis of EM Structures: A Study on One-to-One Transformers

We propose using machine learning models for the direct synthesis of on-...

Please sign up or login with your details

Forgot password? Click here to reset