The explosion of data and the success of Machine Learning have made AI workloads a major driver of modern computing. However, increasing computational demands and energy constraints challenge current processor designs. This project explores new microarchitectural solutions to improve the performance and efficiency of ML applications on general-purpose processors.
This project will address the synergy of machine learning based computing models in Big-data analytics problem, from the perspective of the subjacent computer architecture. The target is to combine efficiently into a single architecture the dissimilar requirements, minimizing the impact on the complex software stack.
This project outlines the necessity of efficiently combining future challenges concerning Big Data with emerging technologies such as non-volatile memories based on nanoscale memristor devices...