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This paper presents a methodology for simultaneous heterogeneous computing, named ENEAC, where a quad core ARM Cortex-A53 CPU works in tandem with a preprogrammed on-board FPGA accelerator. A heterogeneous scheduler distributes the tasks optimally among all the resources and all compute units run asynchronously, which allows for improved performance for irregular workloads. ENEAC achieves up to 17\% performance improvement \ignore{and 14\% energy usage reduction,} when using all platform resources compared to just using the FPGA accelerators and up to 865\% performance increase \ignore{and up to 89\% energy usage decrease} when using just the CPU. The workflow uses existing commercial tools and C/C++ as a single programming language for both accelerator design and CPU programming for improved productivity and ease of verification., Comment: 7 pages, 5 figures, 1 table Presented at the 13th International Workshop on Programmability and Architectures for Heterogeneous Multicores, 2020 (arXiv:2005.07619)

The edge computing paradigm has emerged to handle cloud computing issues such as scalability, security and low response time among others. This new computing trend heavily relies on ubiquitous embedded systems on the edge. Performance and energy consumption are two main factors that should be considered during the design of such systems. Focusing on performance and energy consumption, this paper studies the opportunities and challenges that a heterogeneous embedded system consisting of embedded FPGAs and GPUs (as accelerators) can provide for applications. We study three design, modeling and scheduling challenges throughout the paper. We also propose three techniques to cope with these three challenges. Applying the proposed techniques to three applications including image histogram, dense matrix-vector multiplication and sparse matrix-vector multiplications show 1.79x and 2.29x improvements in performance and energy consumption, respectively, when both FPGA and GPU execute the corresponding application in parallel., Comment: Presented at HIP3ES, 2019

In this paper, we introduce a software-defined framework that enables the parallel utilization of all the programmable processing resources available in heterogeneous system-on-chip (SoC) including FPGA-based hardware accelerators and programmable CPUs. Two platforms with different architectures are considered, and a single C/C++ source code is used in both of them for the CPU and FPGA resources. Instead of simply using the hardware accelerator to offload a task from the CPU, we propose a scheduler that dynamically distributes the tasks among all the resources to fully exploit all computing devices while minimizing load unbalance. The multi-architecture study compares an ARMV7 and ARMV8 implementation with different number and type of CPU cores and also different FPGA micro-architecture and size. We measure that both platforms benefit from having the CPU cores assist FPGA execution at the same level of energy requirements., Comment: Presented at HIP3ES, 2018