论文标题
采矿消息流使用复发的神经网络进行系统芯片设计
Mining Message Flows using Recurrent Neural Networks for System-on-Chip Designs
论文作者
论文摘要
全面的规格对于整个验证连续性的各种活动至关重要。但是,规格通常是模棱两可的,不完整的,甚至包含不一致或错误。本文通过开发一种规范挖掘方法来解决此问题,该方法会自动从SOC交易级别的痕迹中提取顺序模式,从而使挖掘模式集体地表征了SOC设计的系统级规范。这种方法利用了经过收集的SOC执行跟踪训练的长期短期内存(LSTM)网络,以捕获各种通信事件之间的顺序依赖。然后,开发了一种新型算法,以在训练有素的LSTM模型中有效提取系统级通信的顺序模式。还提出了几种痕量处理技术来增强采矿性能。我们评估了非平凡多核SOC原型的模拟轨迹的建议方法。初始结果表明,所提出的方法能够从高度并发的SOC执行跟踪中提取有关系统级规范的各种模式。
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.