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This thesis presents a novel solution for CPU architecture simulation with a primary focus on cache miss prediction using machine learning techniques. The solution consists of two main components: a configurable application designed to generate detailed execution traces via DynamoRIO and a machine learning model, specifically a Long Short-Term Memory (LSTM) network, developed to predict cache behaviors based on these traces. The LSTM model was trained and validated using a comprehensive dataset derived from detailed trace analysis, which included various parameters like instruction sequences and memory access patterns. The model was tested against unseen datasets to evaluate its predictive accuracy and robustness. These tests were critical in demonstrating the model’s effectiveness in real-world scenarios, showing it could reliably predict cache misses with significant accuracy. This validation underscores the viability of machine learning-based methods in enhancing the fidelity of CPU architecture simulations. However, performance tests comparing the LSTM model and DynamoRIO revealed that while the LSTM achieves satisfactory accuracy, it does so at the cost of increased processing time. Specifically, the LSTM model processed 25 million instructions in 45 seconds, compared to DynamoRIO’s 41 seconds, with additional overheads for loading and executing the inference process. This highlights a critical trade-off between accuracy and simulation speed, suggesting areas for further optimization and efficiency improvements in future work.

<p>Our modern society is more dependent on its assets in space now more than ever. For<br> example, the Global Positioning System (GPS) many rely on for navigation uses data from a<br> 24-satellite constellation. Additionally, our current infrastructure for gas pumps, cell phones,<br> ATMs, traffic lights, weather data, etc. all depend on satellite data from various constel-<br> lations. As a result, it is increasingly necessary to accurately track and predict the space<br> domain. In this thesis, after discussing how space object tracking and object position pre-<br> diction is currently being done, I propose a machine learning-based approach to improving<br> the space object position prediction over the standard SGP4 method, which is limited in<br> prediction accuracy time to about 24 hours. Using this approach, we are able to show that<br> meaningful improvements over the standard SGP4 model can be achieved using a machine<br> learning model built based on a type of recurrent neural network called a long short term<br> memory model (LSTM). I also provide distance predictions for 4 different space objects over<br> time frames of 15 and 30 days. Future work in this area is likely to include extending and<br> validating this approach on additional satellites to construct a more general model, testing a<br> wider range of models to determine limits on accuracy across a broad range of time horizons,<br> and proposing similar methods less dependent on antiquated data formats like the TLE.</p>