Synergistic Memory Optimisations: Precision Tuning in Heterogeneous Memory Hierarchies
Gabriele Magnani, Daniele Cattaneo, Lev Denisov, Giuseppe Tagliavini, Giovanni Agosta, Stefano Cherubin · IEEE Transactions on Computers · 2025
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× to 51.1× on AxBench benchmarks that employ trigonometrical functions and 4.23–48.4× on Polybench benchmarks over the baseline HERO platform.