Learning Concise Models from Long Execution Traces
Natasha Yogananda Jeppu,Tom Melham,Daniel Kroening,John O'Leary +3 more
- 01 Jul 2020
- pp 1-6
TL;DR: A new algorithm is described for automatically extracting useful models, as automata, from execution traces of a HW/SW system driven by software exercising a use-case of interest, which leverages modern program synthesis techniques to generate predicates on automaton edges, succinctly describing system behaviour.
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Abstract: models of system-level behaviour have applications in design exploration, analysis, testing and verification. We describe a new algorithm for automatically extracting useful models, as automata, from execution traces of a HW/SW system driven by software exercising a use-case of interest. Our algorithm leverages modern program synthesis techniques to generate predicates on automaton edges, succinctly describing system behaviour. It employs trace segmentation to tackle complexity for long traces. We learn concise models capturing transaction-level, system-wide behaviour—experimentally demonstrating the approach using traces from a variety of sources, including the x86 QEMU virtual platform and the Real-Time Linux kernel.
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Citations
•Proceedings Article
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning
Mohammadhosein Hasanbeig,Natasha Yogananda Jeppu,Alessandro Abate,Tom Melham,Daniel Kroening +4 more
- 18 May 2021
TL;DR: DeepSynth as discussed by the authors is a method for effective training of deep RL agents when the reward is sparse and non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives.
33
•Posted Content
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning
Mohammadhosein Hasanbeig,Natasha Yogananda Jeppu,Alessandro Abate,Tom Melham,Daniel Kroening +4 more
TL;DR: This work synthesises a human-interpretable automaton from trace data generated through exploration of the environment by the deep RL agent, then enriched with the synthesised automaton so that generation of an optimal control policy by deep RL is guided by the discovered structure encoded in the automaton.
13
Mining Message Flows from System-on-Chip Execution Traces
Rubel Ahmed,Hao Zheng,Parijat Mukherjee,Mahesh Ketkar,Jin Yang +4 more
- 07 Apr 2021
TL;DR: FlowMiner as discussed by the authors extracts message flows from SoC execution traces to perform rigorous and thorough validation of system-on-chip (SoC) designs, and includes inference rules and optimization techniques to improve mining performance and reduce mining complexity.
8
•Posted Content
Mining Message Flows from System-on-Chip Execution Traces
TL;DR: This paper proposes a specification mining framework, FlowMiner, that automatically extracts message flows from SoC execution traces and includes inference rules and optimization techniques to improve mining performance and reduce mining complexity.
5
Through the Looking Glass: Automated Design Understanding of SystemC-Based VPs at the ESL
TL;DR: In this article , an automated and fast design understanding approach that enables designers to trace detailed information of the virtual prototypes' structure and behavior is presented. But, this approach still has weaknesses, in particular due to the significant manual effort involved for design understanding, analysis, and modeling tasks which is both time consuming and error-prone.
3
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