Synergistic Memory Optimisations: Precision Tuning in Heterogeneous Memory Hierarchies

Abstract

Balancing energy efficiency and high performance in embedded systems requires fine-tuning hardware and software components to co-optimize their interaction. In this work, we address the automated optimization of memory usage through a compiler toolchain that leverages DMA-aware precision tuning and mathematical function memorization. The proposed solution extends the llvm infrastructure, employing the taffo plugins for precision tuning, with the SeTHet extension for DMA-aware precision tuning and luTHet for automated, DMA-aware mathematical function memorization. We performed an experimental assessment on hero, a heterogeneous platform employing risc-v cores as a parallel accelerator. Our solution enables speedups ranging from $1.5\boldsymbol{\times}$ to $51.1\boldsymbol{\times}$ on AxBench benchmarks that employ trigonometrical functions and $4.23-48.4\boldsymbol{\times}$ on Polybench benchmarks over the baseline hero platform.