A Learning-based Control Scheme for Prioritized SMT Processor

In Simultaneous Multithreading (SMT) processors, multiple threads share hardware resources, which improves the overall utilization of the pipeline, but reduces the throughput due to stalls caused by hardware resource shortage and instruction retention of low-priority threads. In real-time processing, where tasks are processed based on priority, high-priority threads occupy much of the processor pipeline, resulting in lower total throughput compared to SMT which does not use priority.In this paper, we propose a novel learning-based SMT processor for real-time processing. This processor dynamically changes its priority according to the utilization of hardware resources. The proposed processor has a mechanism for autonomous learning and inference, which enables dynamic control in response to resource demand bottlenecks and their changes. There are two inference methods suitable for hard and soft real-time systems. It also learns autonomously to make more optimal predictions. Each functional unit of the proposed processor has a counter that indicates the amount occupied at a particular time, the rate, and the total value. The counter allows learning and inference based on resource utilization, throughput, and instruction loss rate. This allows the overall throughput to be increased without compromising the real-time performance of high-priority threads and allows resources to be allocated to other threads. In addition, simulation results show that the IPC (instructions per clock) rate is higher in soft real-time systems, where high throughput is required in multiple applications.