Multithread Accelerators on FPGAs: A Dataflow-Based Approach

Multithreading is a well-known technique for general-purpose systems to deliver a substantial performance gain, raising resource efficiency by exploiting underutilization periods. With the increase of specialized hardware, resource efficiency became fundamental to master the introduced overhead of such kind of devices. In this work, we propose a model-based approach for designing specialized multithread hardware accelerators. This novel approach exploits dataflow models of applications and tagged tokens to let the resulting hardware support concurrent threads without the need to replicate the whole accelerator. Assessment is carried out over different versions of an accelerator for a compute-intensive step of modern video coding algorithms, under several feeding configurations. Results highlight that the proposed multithread accelerators achieve a valuable tradeoff: saving computational resources with respect to replicated parallel single-thread accelerators, while guaranteeing shorter waiting, response, and elaboration time than a unique single-thread accelerator multiplexed in time