A Performance Modelling-Driven Approach to Hardware Resource Scaling

The continuous demand for higher computational performance and the stagnating developments in the general purpose processor landscape have led to a surge in interest for highly specialized and efficient hardware. Combined with the rising popularity of parameterizable hardware, a new opportunity to optimize these architectures for particular workloads arises, largely driven by the RISC-V Instruction Set Architecture (ISA). This work present an application-specific optimization methodology for general purpose processors, enabling the development of architectures which are faster and more efficient for their designated workloads. Driven by the Cache-Aware Roofline Model (CARM) insights, the methodology guides the configuration of the memory and computational subsystems of the processor. We apply this methodology to two applications, demonstrating up to a 2.67x performance increase and a 1.34x improvement to energy efficiency.