Efficient Stimuli Generation using Reinforcement Learning in Design Verification

• URL: http://arxiv.org/abs/2405.19815v1
• Date: Thu, 30 May 2024 08:23:04 GMT
• Title: Efficient Stimuli Generation using Reinforcement Learning in Design Verification
• Authors: Deepak Narayan Gadde, Thomas Nalapat, Aman Kumar, Djones Lettnin, Wolfgang Kunz, Sebastian Simon,
• Abstract summary: Reinforcement Learning (RL) is proposed to generate efficient stimuli with the help of Reinforcement Learning (RL) to reach the maximum code coverage of the Design Under Verification (DUV) In this paper, a novel methodology is proposed to generate efficient stimuli with the help of Reinforcement Learning (RL) to reach the maximum code coverage of the Design Under Verification (DUV)
• Score: 2.9652396326501864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing design complexity of System-on-Chips (SoCs) has led to significant verification challenges, particularly in meeting coverage targets within a timely manner. At present, coverage closure is heavily dependent on constrained random and coverage driven verification methodologies where the randomized stimuli are bounded to verify certain scenarios and to reach coverage goals. This process is said to be exhaustive and to consume a lot of project time. In this paper, a novel methodology is proposed to generate efficient stimuli with the help of Reinforcement Learning (RL) to reach the maximum code coverage of the Design Under Verification (DUV). Additionally, an automated framework is created using metamodeling to generate a SystemVerilog testbench and an RL environment for any given design. The proposed approach is applied to various designs and the produced results proves that the RL agent provides effective stimuli to achieve code coverage faster in comparison with baseline random simulations. Furthermore, various RL agents and reward schemes are analyzed in our work.

Related papers

• Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide Exploration [13.053013407015628]
  This paper addresses the problem of learning optimal control policies for systems with uncertain dynamics. We propose an accelerated RL algorithm that can learn control policies significantly faster than competitive approaches.
  arXiv  Detail & Related papers  (2024-10-16T00:53:41Z)
• Plug-and-Play Controllable Generation for Discrete Masked Models [27.416952690340903]
  This article makes discrete masked models for the generative modeling of discrete data controllable. We propose a novel plug-and-play framework based on importance sampling that bypasses the need for training a conditional score. Our framework is agnostic to the choice of control criteria, requires no gradient information, and is well-suited for tasks such as posterior sampling, Bayesian inverse problems, and constrained generation.
  arXiv  Detail & Related papers  (2024-10-03T02:00:40Z)
• Adaptive Draft-Verification for Efficient Large Language Model Decoding [24.347886232342862]
  Large language model (LLM) decoding involves generating a sequence of tokens based on a given context. The typical autoregressive decoding method requires a separate forward pass through the model for each token generated. We introduce ADED, which accelerates LLM decoding without requiring fine-tuning.
  arXiv  Detail & Related papers  (2024-06-27T22:20:39Z)
• Random Aggregate Beamforming for Over-the-Air Federated Learning in Large-Scale Networks [66.18765335695414]
  We consider a joint device selection and aggregate beamforming design with the objectives of minimizing the aggregate error and maximizing the number of selected devices. To tackle the problems in a cost-effective manner, we propose a random aggregate beamforming-based scheme. We additionally use analysis to study the obtained aggregate error and the number of the selected devices when the number of devices becomes large.
  arXiv  Detail & Related papers  (2024-02-20T23:59:45Z)
• Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement [67.1393112206885]
  Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. We introduce Entropy-Regularized Token-level Policy Optimization (ETPO), an entropy-augmented RL method tailored for optimizing LLMs at the token level. We assess the effectiveness of ETPO within a simulated environment that models data science code generation as a series of multi-step interactive tasks.
  arXiv  Detail & Related papers  (2024-02-09T07:45:26Z)
• MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks [63.24965775030673]
  We propose a novel Reinforcement Learning (RL) approach to design generic Congestion Control (CC) algorithms. Our solution, MARLIN, uses the Soft Actor-Critic algorithm to maximize both entropy and return. We trained MARLIN on a real network with varying background traffic patterns to overcome the sim-to-real mismatch.
  arXiv  Detail & Related papers  (2023-02-02T18:27:20Z)
• Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration [71.95914457415624]
  Microprocessor architects are increasingly resorting to domain-specific customization in the quest for high-performance and energy-efficiency. We propose an alternative formulation that leverages Multi-Agent RL (MARL) to tackle this problem. Our evaluation shows that the MARL formulation consistently outperforms single-agent RL baselines.
  arXiv  Detail & Related papers  (2022-11-29T17:10:24Z)
• Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality [141.89413461337324]
  Deployment efficiency is an important criterion for many real-world applications of reinforcement learning (RL) We propose a theoretical formulation for deployment-efficient RL (DE-RL) from an "optimization with constraints" perspective.
  arXiv  Detail & Related papers  (2022-02-14T01:31:46Z)
• Ordering-Based Causal Discovery with Reinforcement Learning [31.358145789333825]
  We propose a novel RL-based approach for causal discovery, by incorporating RL into the ordering-based paradigm. We analyze the consistency and computational complexity of the proposed method, and empirically show that a pretrained model can be exploited to accelerate training.
  arXiv  Detail & Related papers  (2021-05-14T03:49:59Z)
• Towards Standardizing Reinforcement Learning Approaches for Stochastic Production Scheduling [77.34726150561087]
  reinforcement learning can be used to solve scheduling problems. Existing studies rely on (sometimes) complex simulations for which the code is unavailable. There is a vast array of RL designs to choose from. standardization of model descriptions - both production setup and RL design - and validation scheme are a prerequisite.
  arXiv  Detail & Related papers  (2021-04-16T16:07:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.