Paper Title
MLSCHED: MACHINE LEARNING BASED JOB SCHEDULER

Abstract
Abstract - This paper introduces MLSched, a novel scheduling scheme utilizing machine learning and deep learning techniques, including LSTM, ANN, and Linear Regression. Targeting heterogeneous multicore systems, MLSched enhances throughput by intelligently predicting thread parameters and IPC values for optimal thread scheduling. Compared to existing schemes, MLSched demonstrates a 1.2X speedup and a 20% improvement in system throughput across Parsec and Splash benchmarks, showcasing the effectiveness of machine learning in computer architecture. Keywords - Heterogeneous multiprocessor, Machine learning, Thread Scheduling, Long Short Term Memory.