Application of Deep Reinforcement Learning to Dynamic Verification of DRAM Designs

Choi, Hyojin; Huh, In; Kim, Seungju; Ko, Jeong-Hoon; Jeong, Changwook; Son, Hyeonsik; Kwon, Ki-Won; Chai, Joonwan; Park, Younsik; Jeong, Jaehoon; Kim, Dae Sin; Choi, Jong Seob

doi:10.1109/dac18074.2021.9586282

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…The authors of [12] claim that, using RL, tests generated by their approach were able to achieve higher coverage values than a state-of-the-art test generator based on genetic algorithms presented in [16]. The study in [17] uses a training policy based on the k-nearest neighbor (K-NN) ML algorithm, approaching deep reinforcement learning (DRL) domain. The authors of [17] show how trained agents using RL mechanisms can achieve a higher coverage value compared with tests written by engineers, given operations performed on a DRAM memory.…”

Section: Related Workmentioning

confidence: 99%

“…The study in [17] uses a training policy based on the k-nearest neighbor (K-NN) ML algorithm, approaching deep reinforcement learning (DRL) domain. The authors of [17] show how trained agents using RL mechanisms can achieve a higher coverage value compared with tests written by engineers, given operations performed on a DRAM memory. One example in [12] and the examples in [17] deal with memory-related verification.…”

Section: Related Workmentioning

confidence: 99%

“…The authors of [17] show how trained agents using RL mechanisms can achieve a higher coverage value compared with tests written by engineers, given operations performed on a DRAM memory. One example in [12] and the examples in [17] deal with memory-related verification. The present paper aims to enable the automation of functional verification with RL for a wide range of devices with dynamic functionality.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Reinforcement Learning Made Affordable for Hardware Verification Engineers

Dinu

Ogrutan

2022

Micromachines

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Constrained random stimulus generation is no longer sufficient to fully simulate the functionality of a digital design. The increasing complexity of today’s hardware devices must be supported by powerful development and simulation environments, powerful computational mechanisms, and appropriate software to exploit them. Reinforcement learning, a powerful technique belonging to the field of artificial intelligence, provides the means to efficiently exploit computational resources to find even the least obvious correlations between configuration parameters, stimuli applied to digital design inputs, and their functional states. This paper, in which a novel software system is used to simplify the analysis of simulation outputs and the generation of input stimuli through reinforcement learning methods, provides important details about the setup of the proposed method to automate the verification process. By understanding how to properly configure a reinforcement algorithm to fit the specifics of a digital design, verification engineers can more quickly adopt this automated and efficient stimulus generation method (compared with classical verification) to bring the digital design to a desired functional state. The results obtained are most promising, with even 52 times fewer steps needed to reach a target state using reinforcement learning than when constrained random stimulus generation was used.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Reinforcement Learning Made Affordable for Hardware Verification Engineers

Dinu

Ogrutan

2022

Micromachines

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“…Most data in the semiconductor fields, however, is not available due to high measurement costs. Thus, data-driven approaches have been applied to limited applications such as chip design automation, 3) test vector generation, 4) process optimization, 5) and defect detection. 6,7) Such studies have something in common they can gather massive data, readily.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive studies on deep learning applicable to TCAD

Myung

Choi

Jang

et al. 2023

Jpn. J. Appl. Phys.

Self Cite

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TCAD simulation has incessantly solved many complex problems, but it becomes demanding that alternatives be found because TCAD simulation cannot provide the precise and fast prediction in the nano-scale era. With the success story on deep learning in research area, many big data companies have attempted to introduce deep learning to support or replace TCAD simulation. The reason is deep learning models has great potential that solves the problems of the TCAD simulation in terms of execution time, coverage. This paper aims to describe various scenarios of deep learning applicable to TCAD. We firstly describe an application that supplies TCAD data to the deep learning model although TCAD simulation is not calibrated. We then review various approaches that mimic TCAD simulation itself. We finally introduce an application that deep learning model automatically calibrates TCAD models to the measurement without experts. In each scenario, we review the related papers and compare pros and cons.

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Generating Efficient Instruction Sequence for Software-Based Self-Testing of Processor Cores Using Reinforcement Learning

Seo,

Cho

2024

IEEE Access

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Application of Deep Reinforcement Learning to Dynamic Verification of DRAM Designs

Cited by 5 publications

References 9 publications

Reinforcement Learning Made Affordable for Hardware Verification Engineers

Reinforcement Learning Made Affordable for Hardware Verification Engineers

Comprehensive studies on deep learning applicable to TCAD

Generating Efficient Instruction Sequence for Software-Based Self-Testing of Processor Cores Using Reinforcement Learning

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