Investigating Machine Learning for verification of AMBA APB protocol.

Detta är en Master-uppsats från Lunds universitet/Institutionen för elektro- och informationsteknik

Sammanfattning: It is a well-known fact that in any Application Specific Integrated Circuit (ASIC) design, verification consumes most time and resources. And when it comes to huge designs, finding bugs can be tedious given the area and the complexity. As per Moore’s law, the design complexity is increasing exponentially due to the growing demand for performance. Therefore, On-Chip communication becomes crucial. The interconnects play a vital role in communication between two Intellectual Properties (IP) in a System-on-Chip (SOC), which makes it an utmost priority to verify the protocol. In order to achieve this, many test-scenarios are developed which in turn increases the debug effort and verification space. As Advanced Microcontroller Bus Architecture (AMBA) protocol is most commonly used as a communication protocol, the Design Under Test (DUT) for this thesis is Advanced Peripheral Bus (APB), a member of the AMBA family. This thesis aims to investigate the applications of Machine Learning (ML) to reduce the overall verification time and effort. Basic classifiers such as K-Nearest Neighbors (KNN), Decision Trees (DT) are explored and studied, along with two types of Neural Networks, such as the FeedForward Neural Network (FFNN) and Recurrent Neural Network (RNN). These algorithms were trained overtime with various datasets along with fine-tuning their respective parameters. The Long Short Term Memory (LSTM) model, a variant of the RNN is the preferred among other models as it is capable of learning the complete behavior of the APB. From the results obtained, the LSTM was able to classify the write, read and the failed transactions with an accuracy of 90%. The results also discusses the accuracy obtained by other models and compares the time and effort taken to implement all of them. The study is concluded with a belief that ML can be a method in verification with suggested improvements. The ideas for future studies have been briefly presented as well.

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