Julio Costella Vicenzi

Cloud Environments have been constantly adopting collaborative CPU-FPGA architectures to accelerate applications by partitioning the execution of their kernels across both devices. However, exploiting the optimization techniques that both archi-tectures offer is challenging, so they must be smartly employed depending on the application at hand and the target optimization (e.g., performance or energy). Given that, this work investigates the impact of collaboratively applying thread throttling (i.e. artificially decreasing the number of active threads) on the CPU side and HLS (High-Level Synthesis)-versioning on the FPGA side. We use a multi-tenant Cloud service as our object of study, where sequence of application requests with different priorities result in DAGs of application kernels that must be executed over the heterogeneous architecture. We show that by synergistically applying thread throttling and HLS-versioning to the incoming kernels may improve the Energy-Dealy product in up to 41x over the default and non-optimized execution.

Shapelet-based methods have emerged as accurate and interpretable tools for time-series classification in machine learning applications. Although these methods find wide applicability, nowadays their use is restricted due to the heavy computational burden associated with numerous computations of z-score normalization and Euclidean distance. The current article addresses this issue with the proposal of a parameterizable parallel hardware accelerator to perform the aforementioned computations. Results show that our dedicated shapelet distance engine can significantly reduce the runtime when compared with a software implementation, achieving speedups of up to five times in terms of clock cycles.