Survey of Machine Learning for Electronic Design Automation

Gubbi, Kevin Immanuel; Beheshti-Shirazi, Sayed Aresh; Sheaves, Tyler; Salehi, Soheil; PD, Sai Manoj; Rafatirad, Setareh; Sasan, Avesta; Homayoun, Houman

doi:10.1145/3526241.3530834

Cited by 19 publications

(6 citation statements)

References 21 publications

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“…Recent developments in the application of machine learning approaches to design research challenges have generated a lot of interest [10,11]. A model is trained or guided by the actual application of a process or phenomena, and then it is used to predict the same metric for new input…”

Section: Machine Learning Techniques In Embedded Memorymentioning

confidence: 99%

Parasitic RC Estimation and Defect Prediction for Embedded Memory using Machine Learning

Maddela,

Sinha,

Parvathi

et al. 2024

Preprint

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In today's rapidly scaling-down technological environment, identifying the best-fit algorithms for evaluating complicated circuits such as SRAMs is a difficult issue. Many fault models have developed, however their flexibility of use is limited by the restrictions and constraints of the provided test environment. The majority of existing fault models have been studied in terms of well-known March algorithms, which simply provide fault detection information. Scaled-down technologies have an impact on parasitic effects as well, resulting in an extra source of defective behavior and making current test algorithms vulnerable to them. Recent work that uses method of parasitic extraction for fault detection have addressed the problem of limitation due to scale down technologies. However, as the circuit complexity increases the estimation of RC would be tedious. Hence in this paper machine learning based parasitic RC extraction is proposed. Also, as an extension to that, proposed ML based fault detection using extracted parasitic RCs as dataset. The proposed machine learning based fault prediction uses extracted parasitic RCs as dataset. The parasitic RC values are extracted for each fault model using technologies of 120nm down to deep submicron 7nm. Regression algorithm is used for modeling the machine for extraction of RCs and observed that 88% of prediction accuracy. Decision tree modeling is used for fault detection and observed 91.7% of accuracy in prediction of fault. 3.1 Effect of open defects in 6T SRAM Cell In this are article we have consider the node to node open/short faults. In Fig 2. We have imposed all possible open defects and then we have analyzed the memory cell for all possible open defects. There are totally 25 open defects are possible as shown in the fig 2. The simulation results and different types faults occurs for all open defects are shown in table 1 Fig2: Fault model for Open Defects in 6T-SRAM Cell Table 1. 6T SRAM Cell open defect list for different technologies

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Section: Machine Learning Techniques In Embedded Memorymentioning

confidence: 99%

Parasitic RC Estimation and Defect Prediction for Embedded Memory using Machine Learning

Maddela,

Sinha,

Parvathi

et al. 2024

Preprint

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“…Machine learning techniques are increasingly successfully applied for classical circuit design automation [6]. Reinforcement Learning (RL) for the compilation of quantum gate sequences is presented in [7].…”

Section: A Motivationmentioning

confidence: 99%

Graph Neural Network Autoencoders for Efficient Quantum Circuit Optimisation

Moflic¹,

Garg²

2023

Preprint

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Reinforcement learning (RL) is a promising method for quantum circuit optimisation. However, the state space that has to be explored by an RL agent is extremely large when considering all the possibilities in which a quantum circuit can be transformed through local rewrite operations. This state space explosion slows down the learning of RL-based optimisation strategies. We present for the first time how to use graph neural network (GNN) autoencoders for the optimisation of quantum circuits. We construct directed acyclic graphs from the quantum circuits, encode the graphs and use the encodings to represent RL states. We illustrate our proof of concept implementation on Bernstein-Vazirani circuits and, from preliminary results, we conclude that our autoencoder approach: a) maintains the optimality of the original RL method; b) reduces by 20 % the size of the table that encodes the learned optimisation strategy. Our method is the first realistic first step towards very large scale RL quantum circuit optimisation.

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“…Modelos de IA generativa, como ChatGPT e Dall-E, são capazes de aprender padrões a partir de grandes massas de dados (imagens, textos, etc) e gerar saídas que reproduzem esses padrões de forma verossímil. Algumas aplicações representativas de IA em design de produtos incluem o projeto de circuitos integrados [Gubbi et al 2022, Wang and Luo 2019, Hamolia and Melnyk 2021, desenvolvimento de novas drogas [Grisoni et al 2021], desenvolvimento de peças de vestuário na indústria da moda [Liang et al 2020, Giri et al 2019, desenvolvimento de produtos na indústria alimentícia , etc. É importante salientar que as aplicações industriais de técnicas de IA são vastas, abrangendo muitos setores e muitas tarefas diferentes, de modo que nesta seção são discutidos apenas alguns exemplos.…”

Section: Aplicaçõesunclassified

“A Nova Eletricidade”: Aplicações, Riscos e Tendências da IA Moderna

Bazzan,

Tavares,

Pereira

et al. 2023

Escola De Computação PPGC/UFRGS 50 Anos: Transformando Desafios Em Oportunidades Para O Futuro

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A provocativa comparação entre IA e eletricidade, feita pelo cientista da computação e empreendedor Andrew Ng, resume a profunda transformação que os recentes avanços em Inteligência Artificial (IA) têm desencadeado no mundo. Este capítulo apresenta uma visão geral pela paisagem em constante evolução da IA. Sem pretensões de exaurir o assunto, exploramos as aplicações que estão redefinindo setores da economia, impactando a sociedade e a humanidade. Analisamos os riscos que acompanham o rápido progresso tecnológico e as tendências futuras da IA, área que trilha o caminho para se tornar uma tecnologia de propósito geral, assim como a eletricidade, que revolucionou a sociedade dos séculos XIX e XX.

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Survey of Machine Learning for Electronic Design Automation

Cited by 19 publications

References 21 publications

Parasitic RC Estimation and Defect Prediction for Embedded Memory using Machine Learning

Parasitic RC Estimation and Defect Prediction for Embedded Memory using Machine Learning

Graph Neural Network Autoencoders for Efficient Quantum Circuit Optimisation

“A Nova Eletricidade”: Aplicações, Riscos e Tendências da IA Moderna

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