Procedural- and Reinforcement-Learning-Based Automation Methods for Analog Integrated Circuit Sizing in the Electrical Design Space

Analog integrated circuit sizing is notoriously difficult to automate due to its complexity and scale; thus, it continues to heavily rely on human expert knowledge. This work presents a machine learning-based design automation methodology comprising pre-defined building blocks such as current mirror...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yannick Uhlmann [verfasserIn] Michael Brunner [verfasserIn] Lennart Bramlage [verfasserIn] Jürgen Scheible [verfasserIn] Cristóbal Curio [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	analog IC design machine learning reinforcement learning GM over ID procedural design automation learning-based design automation

Übergeordnetes Werk:	In: Electronics - MDPI AG, 2013, 12(2023), 2, p 302
Übergeordnetes Werk:	volume:12 ; year:2023 ; number:2, p 302

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/electronics12020302

Katalog-ID:	DOAJ081815212

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520			\|a Analog integrated circuit sizing is notoriously difficult to automate due to its complexity and scale; thus, it continues to heavily rely on human expert knowledge. This work presents a machine learning-based design automation methodology comprising pre-defined building blocks such as current mirrors or differential pairs and pre-computed look-up tables for electrical characteristics of primitive devices. Modeling the behavior of primitive devices around the operating point with neural networks combines the speed of equation-based methods with the accuracy of simulation-based approaches and, thereby, brings quality of life improvements for analog circuit designers using the <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msub<<mi<g</mi<<mi mathvariant="normal"<m</mi<</msub<<mo</</mo<<msub<<mi<I</mi<<mi mathvariant="normal"<d</mi<</msub<</mrow<</semantics<</math<</inline-formula< method. Extending this procedural automation method for human design experts, we present a fully autonomous sizing approach. Related work shows that the convergence properties of conventional optimization approaches improve significantly when acting in the electrical domain instead of the geometrical domain. We, therefore, formulate the circuit sizing task as a sequential decision-making problem in the alternative electrical design space. Our automation approach is based entirely on reinforcement learning, whereby abstract agents learn efficient design space navigation through interaction and without expert guidance. These agents’ learning behavior and performance are evaluated on circuits of varying complexity and different technologies, showing both the feasibility and portability of the work presented here.
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language	English
source	In Electronics 12(2023), 2, p 302 volume:12 year:2023 number:2, p 302
sourceStr	In Electronics 12(2023), 2, p 302 volume:12 year:2023 number:2, p 302
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authorswithroles_txt_mv	Yannick Uhlmann @@aut@@ Michael Brunner @@aut@@ Lennart Bramlage @@aut@@ Jürgen Scheible @@aut@@ Cristóbal Curio @@aut@@
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callnumber-first	T - Technology
author	Yannick Uhlmann
spellingShingle	Yannick Uhlmann misc TK7800-8360 misc analog IC design misc machine learning misc reinforcement learning misc GM over ID misc procedural design automation misc learning-based design automation misc Electronics Procedural- and Reinforcement-Learning-Based Automation Methods for Analog Integrated Circuit Sizing in the Electrical Design Space
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topic_title	TK7800-8360 Procedural- and Reinforcement-Learning-Based Automation Methods for Analog Integrated Circuit Sizing in the Electrical Design Space analog IC design machine learning reinforcement learning GM over ID procedural design automation learning-based design automation
topic	misc TK7800-8360 misc analog IC design misc machine learning misc reinforcement learning misc GM over ID misc procedural design automation misc learning-based design automation misc Electronics
topic_unstemmed	misc TK7800-8360 misc analog IC design misc machine learning misc reinforcement learning misc GM over ID misc procedural design automation misc learning-based design automation misc Electronics
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title	Procedural- and Reinforcement-Learning-Based Automation Methods for Analog Integrated Circuit Sizing in the Electrical Design Space
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title_full	Procedural- and Reinforcement-Learning-Based Automation Methods for Analog Integrated Circuit Sizing in the Electrical Design Space
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title_sort	procedural- and reinforcement-learning-based automation methods for analog integrated circuit sizing in the electrical design space
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title_auth	Procedural- and Reinforcement-Learning-Based Automation Methods for Analog Integrated Circuit Sizing in the Electrical Design Space
abstract	Analog integrated circuit sizing is notoriously difficult to automate due to its complexity and scale; thus, it continues to heavily rely on human expert knowledge. This work presents a machine learning-based design automation methodology comprising pre-defined building blocks such as current mirrors or differential pairs and pre-computed look-up tables for electrical characteristics of primitive devices. Modeling the behavior of primitive devices around the operating point with neural networks combines the speed of equation-based methods with the accuracy of simulation-based approaches and, thereby, brings quality of life improvements for analog circuit designers using the <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msub<<mi<g</mi<<mi mathvariant="normal"<m</mi<</msub<<mo</</mo<<msub<<mi<I</mi<<mi mathvariant="normal"<d</mi<</msub<</mrow<</semantics<</math<</inline-formula< method. Extending this procedural automation method for human design experts, we present a fully autonomous sizing approach. Related work shows that the convergence properties of conventional optimization approaches improve significantly when acting in the electrical domain instead of the geometrical domain. We, therefore, formulate the circuit sizing task as a sequential decision-making problem in the alternative electrical design space. Our automation approach is based entirely on reinforcement learning, whereby abstract agents learn efficient design space navigation through interaction and without expert guidance. These agents’ learning behavior and performance are evaluated on circuits of varying complexity and different technologies, showing both the feasibility and portability of the work presented here.
abstractGer	Analog integrated circuit sizing is notoriously difficult to automate due to its complexity and scale; thus, it continues to heavily rely on human expert knowledge. This work presents a machine learning-based design automation methodology comprising pre-defined building blocks such as current mirrors or differential pairs and pre-computed look-up tables for electrical characteristics of primitive devices. Modeling the behavior of primitive devices around the operating point with neural networks combines the speed of equation-based methods with the accuracy of simulation-based approaches and, thereby, brings quality of life improvements for analog circuit designers using the <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msub<<mi<g</mi<<mi mathvariant="normal"<m</mi<</msub<<mo</</mo<<msub<<mi<I</mi<<mi mathvariant="normal"<d</mi<</msub<</mrow<</semantics<</math<</inline-formula< method. Extending this procedural automation method for human design experts, we present a fully autonomous sizing approach. Related work shows that the convergence properties of conventional optimization approaches improve significantly when acting in the electrical domain instead of the geometrical domain. We, therefore, formulate the circuit sizing task as a sequential decision-making problem in the alternative electrical design space. Our automation approach is based entirely on reinforcement learning, whereby abstract agents learn efficient design space navigation through interaction and without expert guidance. These agents’ learning behavior and performance are evaluated on circuits of varying complexity and different technologies, showing both the feasibility and portability of the work presented here.
abstract_unstemmed	Analog integrated circuit sizing is notoriously difficult to automate due to its complexity and scale; thus, it continues to heavily rely on human expert knowledge. This work presents a machine learning-based design automation methodology comprising pre-defined building blocks such as current mirrors or differential pairs and pre-computed look-up tables for electrical characteristics of primitive devices. Modeling the behavior of primitive devices around the operating point with neural networks combines the speed of equation-based methods with the accuracy of simulation-based approaches and, thereby, brings quality of life improvements for analog circuit designers using the <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<mrow<<msub<<mi<g</mi<<mi mathvariant="normal"<m</mi<</msub<<mo</</mo<<msub<<mi<I</mi<<mi mathvariant="normal"<d</mi<</msub<</mrow<</semantics<</math<</inline-formula< method. Extending this procedural automation method for human design experts, we present a fully autonomous sizing approach. Related work shows that the convergence properties of conventional optimization approaches improve significantly when acting in the electrical domain instead of the geometrical domain. We, therefore, formulate the circuit sizing task as a sequential decision-making problem in the alternative electrical design space. Our automation approach is based entirely on reinforcement learning, whereby abstract agents learn efficient design space navigation through interaction and without expert guidance. These agents’ learning behavior and performance are evaluated on circuits of varying complexity and different technologies, showing both the feasibility and portability of the work presented here.
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title_short	Procedural- and Reinforcement-Learning-Based Automation Methods for Analog Integrated Circuit Sizing in the Electrical Design Space
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Procedural- and Reinforcement-Learning-Based Automation Methods for Analog Integrated Circuit Sizing in the Electrical Design Space

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