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AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs
[article]
2020
arXiv
pre-print
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This work presents AutoCkt, a machine learning optimization framework trained using deep reinforcement learning that not only finds post-layout circuit parameters for a given target specification, but ...
Using the Berkeley Analog Generator, AutoCkt is able to design 40 LVS passed operational amplifiers in 68 hours, 9.6X faster than the state-of-the-art when considering layout parasitics. ...
Our Contributions Inspired by the sequential thought process used by expert analog designers, we present AutoCkt, a machine learning framework to solve analog circuits. ...
arXiv:2001.01808v2
fatcat:5gq6flvajnahphzguaxswsdynm
Fast Design Space Adaptation with Deep Reinforcement Learning for Analog Circuit Sizing
[article]
2020
arXiv
pre-print
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
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We present a novel framework for design space search on analog circuit sizing using deep reinforcement learning (DRL). ...
Nowadays, analog circuit design is a manual routine that requires heavy design efforts due to the absence of automation tools, motivating the urge to develop one. ...
Recently introduced methods primarily leverage the current success in deep learning. ...
arXiv:2009.13772v3
fatcat:iy3wk7txpbhctotreakhpb43g4
DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Analog circuit sizing takes a significant amount of manual effort in a typical design cycle. ...
This paper presents DNN-Opt, a Reinforcement Learning (RL) inspired Deep Neural Network (DNN) based black-box optimization framework for analog circuit sizing. ...
Index Terms-Analog Circuit Sizing Automation, Blackbox Optimization, Reinforcement Learning, Deep Neural Network I. ...
arXiv:2110.00211v1
fatcat:ntui74flwzbipn6yntf64n4xu4
A Survey of Machine Learning for Computer Architecture and Systems
[article]
2021
arXiv
pre-print
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
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2021 accepted doi:10.1145/3494523 arXiv:2102.07952v2 fatcat:xevdv2ta2vapfop6k6cfbuihny
It has been a long time that computer architecture and systems are optimized to enable efficient execution of machine learning (ML) algorithms or models. ...
For ML-based modelling, we discuss existing studies based on their target level of system, ranging from the circuit level to the architecture/system level. ...
Performance Modeling in Chip Design and Design Automation 3.3.1 Analog Circuit Analysis. ...
arXiv:2102.07952v1
fatcat:vzj776a6abesljetqobakoc3dq