Tensor Optimization for High-Level Synthesis Design Flows

Improving data locality of tensor data structures is a crucial optimization for maximizing the performance of Machine Learning and intensive Linear Algebra applications. While CPUs and GPUs improve data locality by means of automated caching mechanisms, FPGAs let the developer specify data structure allocation. Although this feature enables a high degree of customizability, the increasing complexity and memory footprint of modern applications prevent considering any manual approach to find an optimal allocation. For this reason, we propose a compiler optimization to automatically improve the tensor allocation of high-level software descriptions. The optimization is controlled by a flexible cost model that can be tuned by means of simple yet expressive callback functions. In this way, the user can tailor the optimization strategy with respect to the optimization goal. We tested our methodology integrating our optimization in the Bambu open-source HLS framework. In this setting, we achieved a 14% speedup on the digit recognition version proposed by the Rosetta benchmark. Moreover, we tested our optimization on the CHStone benchmark suite, achieving an average of 6% speedup. Finally, we applied our methodology on two industrial examples from the aerospace domain obtaining a 15% speedup. As a final step, we tested the versatility of our methodology inserting our optimization in the Clang software optimization flow achieving a 12% speedup on the Rosetta benchmark when running on CPU.