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DTSTAMP:20211208T164506Z
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DTSTART;TZID=America/Los_Angeles:20211209T153000
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UID:dac_DAC 2021_sess128@linklings.com
SUMMARY:Reinforced: Analog Circuit Sizing and Layout
DESCRIPTION:Research Manuscript\n\nThis session starts with four inspiring
  papers to boost the analog and mixed-signal circuit sizing and optimizati
 on process with various flavors of reinforcement learning and Bayesian opt
 imization, as for instance using deep neural network, localized and trust-
 based modeling and exploration, and exploiting design knowledge. The last 
 two papers present exciting ideas on unsupervised symmetry constraint extr
 action and interactive analog layout editing with legalization.\n\nPriorit
 ized Reinforcement Learning for Analog Circuit Optimization With Design Kn
 owledge\n\nNS\n\nAnalog circuit design relies on time-consuming manual des
 ign by experts. The development of RL algorithms draws attention to potent
 ial applications in circuit optimization. However, for robust analog circu
 it design, a rapid search for high-quality design-points is more desired t
 han finding a globall...\n\n---------------------\nUniversal Symmetry Cons
 traint Extraction for Analog and Mixed-Signal Circuits with Graph Neural N
 etworks\n\nChen\n\nRecent research trends in analog layout synthesis aim f
 or a fully automated netlist-to-GDSII design flow with minimum human effor
 ts.\nSymmetry matching between critical building blocks and devices can si
 gnificantly impact the overall circuit performance.\nTherefore, providing 
 accurate symmetry constra...\n\n---------------------\nInteractive Analog 
 Layout Editing with Instant Placement Legalization\n\nLiu, Gao\n\nAnalog l
 ayout design still relies heavily on manual efforts. Current fully automat
 ed flows are not yet able to satisfy the demands of versatile customizatio
 n and not compatible to the existing manual flows. Interactive layout edit
 ing has the potential to bridge the gap between the manual flows and f...\
 n\n---------------------\nDNN-Opt: An RL Inspired Optimization for Analog 
 Circuit Sizing using Deep Neural Networks*\n\nBudak\n\nIn this paper, we p
 resent DNN-Opt, a novel Deep Neural Network (DNN) based black-box optimiza
 tion framework for analog sizing. Our method outperforms other black-box o
 ptimization methods on small building blocks and large industrial circuits
  with significantly fewer simulations and better performanc...\n\n\nTopic:
  EDA\n\nKeyword: Analog Design, Simulation, Verification and Test\n\nVirtu
 al Program: Presented In-Person
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