Feature-based Signal Selection for Post-silicon Debug using Machine Learning
نویسندگان
چکیده
A key challenge of post-silicon validation methodology is to select a limited number of trace signals that are effective during post-silicon debug. Structural analysis used by traditional signal selection techniques are fast but lead to poor restoration quality. In contrast, simulation-based selection techniques provide superior restorability but incur significant computation overhead. While early work on machine learning based signal selection is promising [1], it is still not applicable on large industrial designs since it needs thousands of simulations of large and complex designs. In this paper, we propose a signal selection technique that addresses the scalability issue of simulation-based techniques while maintaining a high restoration performance. The basic idea is to train a machine learning framework using a small set of circuits, and apply the trained model to the bigger circuit under test, without any need for simulating the large industry-scale designs. This paper makes two fundamental contributions: i) this is the first attempt to show that learning from small related circuits can be useful for signal selection, and ii) this is the first automated signal selection approach that is applicable on industrial designs without sacrificing restoration quality. Experimental results indicate that our approach can improve restorability by up to 135.4% (8.8% on average) while significantly reduce (up to 37X, 16.6X on average) the runtime compared to existing signal selection approaches.
منابع مشابه
Feature-based Signal Selection for Post-silicon Debug using Machine Learning
A key challenge of post-silicon validation methodology is to select a limited number of trace signals that are effective during post-silicon debug. Structural analysis used by traditional signal selection techniques are fast but lead to poor restoration quality. In contrast, simulation-based selection techniques provide superior restorability but incur significant computation overhead. While ea...
متن کاملMental Arithmetic Task Recognition Using Effective Connectivity and Hierarchical Feature Selection From EEG Signals
Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recogni...
متن کاملStock Price Prediction using Machine Learning and Swarm Intelligence
Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this...
متن کاملMachine learning based Visual Evoked Potential (VEP) Signals Recognition
Introduction: Visual evoked potentials contain certain diagnostic information which have proved to be of importance in the visual systems functional integrity. Due to substantial decrease of amplitude in extra macular stimulation in commonly used pattern VEPs, differentiating normal and abnormal signals can prove to be quite an obstacle. Due to developments of use of machine l...
متن کاملClassification of Right/Left Hand Motor Imagery by Effective Connectivity Based on Transfer Entropy in EEG Signal
The right and left hand Motor Imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchi...
متن کامل