FARMER: Online-Learning-Based Workload Consolidation on Large FPGAs Accelerated With Dynamic Partial Reconfiguration

As the demand for performance and scalability in cloud applications continues to grow, high-performance computing (HPC) facilities increasingly integrate FPGAs to accelerate computational workloads. To fully utilize the extensive resources available on modern high-end FPGAs, it is essential to optimize the allocation of multiple applications on a single device. This article introduces FARMER, a novel online learning methodology that leverages machine learning (ML) to model the throughput of different applications running concurrently on the same FPGA. It combines this with a sequential decision-making strategy and an in-circuit exploration flow based on dynamic partial reconfiguration (DPR) to drastically speed up the exploration of large design spaces. Experimental evaluations across a wide range of representative scenarios, conducted on a real prototyping platform using an AMD Alveo U55C FPGA board, demonstrate that FARMER consistently identifies a feasible solution while exploring less than $\mathbf {0.012\%}$ of the total design space.