Convex models for accelerating applications on FPGA-based clusters

We propose a new approach, based on a set of convex models, to accelerate an application using a computing cluster which contains field-programmable gate arrays (FPGAs). The computationally-intensive tasks of the application are mapped onto multiple acceleration nodes, and the datapaths on the nodes are customized around the tasks during compilation. We propose models for computation and communication on the FPGA-based cluster, and formulate the design problem as a convex non-linear optimization problem allowing design exploration. We evaluate our approach on a cluster with 16 nodes for Monte Carlo simulation, resulting in a design 690 times faster than a software implementation.