학술논문

Mining Message Flows using Recurrent Neural Networks for System-on-Chip Designs
Document Type
Conference
Source
2020 21st International Symposium on Quality Electronic Design (ISQED) Quality Electronic Design (ISQED), 2020 21st International Symposium on. :389-394 Mar, 2020
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Recurrent neural networks
Multicore processing
Prototypes
System-on-chip
Data mining
Language
Abstract
Comprehensive specifications are essential for various activities across the entire validation continuum for system-on-chip (SoC) designs. However, specifications are often ambiguous, incomplete, or even contain inconsistencies or errors. This paper addresses this problem by developing a specification mining approach that automatically extracts sequential patterns from SoC transaction-level traces such that the mined patterns collectively characterize system-level specifications for SoC designs. This approach exploits long short-term memory (LSTM) networks trained with the collected SoC execution traces to capture sequential dependencies among various communication events. Then, a novel algorithm is developed to efficiently extract sequential patterns on system-level communications from the trained LSTM models. Several trace processing techniques are also proposed to enhance the mining performance. We evaluate the proposed approach on simulation traces of a non-trivial multi-core SoC prototype. Initial results show that the proposed approach is capable of extracting various patterns on system-level specifications from the highly concurrent SoC execution traces.