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        <title>From Words to Wires: Generating Functioning Electronic Devices from Natural Language Descriptions</title>
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        <dateIssued>2023-12</dateIssued>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
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            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
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            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
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        <name type="personal">
            <namePart type="given">Kalika</namePart>
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    <abstract>In this work, we show that contemporary language models have a previously unknown skill – the capacity for electronic circuit design from high-level textual descriptions, akin to code generation. We introduce two benchmarks: PINS100, assessing model knowledge of electrical components, and MICRO25, evaluating a model’s capability to design common microcontroller circuits and code in the Arduino ecosystem that involve input, output, sensors, motors, protocols, and logic – with models such as GPT-4 and Claude-V1 achieving between 60% to 96% Pass@1 on generating full devices. We include six case studies of using language models as a design assistant for moderately complex devices, such as a radiation-powered random number generator, an emoji keyboard, a visible spectrometer, and several assistive devices, while offering a qualitative analysis performance, outlining evaluation challenges, and suggesting areas of development to improve complex circuit design and practical utility. With this work, we aim to spur research at the juncture of natural language processing and electronic design.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-emnlp.864</identifier>
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%0 Conference Proceedings
%T From Words to Wires: Generating Functioning Electronic Devices from Natural Language Descriptions
%A Jansen, Peter
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F jansen-2023-words
%X In this work, we show that contemporary language models have a previously unknown skill – the capacity for electronic circuit design from high-level textual descriptions, akin to code generation. We introduce two benchmarks: PINS100, assessing model knowledge of electrical components, and MICRO25, evaluating a model’s capability to design common microcontroller circuits and code in the Arduino ecosystem that involve input, output, sensors, motors, protocols, and logic – with models such as GPT-4 and Claude-V1 achieving between 60% to 96% Pass@1 on generating full devices. We include six case studies of using language models as a design assistant for moderately complex devices, such as a radiation-powered random number generator, an emoji keyboard, a visible spectrometer, and several assistive devices, while offering a qualitative analysis performance, outlining evaluation challenges, and suggesting areas of development to improve complex circuit design and practical utility. With this work, we aim to spur research at the juncture of natural language processing and electronic design.
%R 10.18653/v1/2023.findings-emnlp.864
%U https://aclanthology.org/2023.findings-emnlp.864
%U https://doi.org/10.18653/v1/2023.findings-emnlp.864
%P 12972-12990

Markdown (Informal)

[From Words to Wires: Generating Functioning Electronic Devices from Natural Language Descriptions](https://aclanthology.org/2023.findings-emnlp.864) (Jansen, Findings 2023)

• From Words to Wires: Generating Functioning Electronic Devices from Natural Language Descriptions (Jansen, Findings 2023)

ACL

• Peter Jansen. 2023. From Words to Wires: Generating Functioning Electronic Devices from Natural Language Descriptions. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 12972–12990, Singapore. Association for Computational Linguistics.