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Adoption of reconfigurable computing is limited in part by the lack of simplified, economic, and reusable solutions. The significant speedup and energy saving can increase performance but also design complexity; in particular for heterogeneous SoCs blending several CPUs, GPUs, and FPGAAccelerator Cores. On the other hand, implementing complex algorithms in hardware requires modeling and verification, not only HDL generation. Most approaches are too specific without looking for reusability. Therefore, we present a solution based on: (1) a design methodology to develop algorithms accelerated in reconfigurable/non-reconfigurable IP-Cores, using common access tools, and contemplating verification from model to embedded software stages; (2) a generic accelerator core design that enables relocation and reuse almost independently of the algorithm, and data-flow driven execution models; and (3) a performance analysis of the acceleration mechanisms included in our system (i.e., accelerator core, burst I/O transfers, and reconfiguration pre-fetch). In consequence, the implemented system accelerates algorithms (e.g., FIR and Kalman filters) with speedups up to 3 orders of magnitude, compared to processor implementations.

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