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AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs [article]

Keertana Settaluri, Ameer Haj-Ali, Qijing Huang, Kourosh Hakhamaneshi, Borivoje Nikolic
2020 arXiv   pre-print
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]

Kai-En Yang, Chia-Yu Tsai, Hung-Hao Shen, Chen-Feng Chiang, Feng-Ming Tsai, Chung-An Wang, Yiju Ting, Chia-Shun Yeh, Chin-Tang Lai
2020 arXiv   pre-print
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]

Ahmet F. Budak, Prateek Bhansali, Bo Liu, Nan Sun, David Z. Pan, Chandramouli V. Kashyap
2021 arXiv   pre-print
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]

Nan Wu, Yuan Xie
2021 arXiv   pre-print
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