Design Space Exploration for Scheduling and Allocation in High Level Synthesis of Datapaths

High-Level Synthesis (HLS) has enabled users to rapidly develop designs targeted for FPGAs from the behavioral description of the design. However, to synthesize an optimal design capable of taking better advantage of the target FPGA, a considerable amount of effort is needed to transform the initial behavioral description into a form that can capture the desired level of parallelism. Thus, a design space exploration (DSE) engine capable of optimizing large complex designs is needed to achieve this goal. We present a new DSE engine capable of considering code transformation, compiler directives (pragmas), and the compatibility of these optimizations. To accomplish this, we initially express the structure of the input code as a graph to guide the exploration process. To appropriately transform the code, we take advantage of ScaleHLS based on the multi-level compiler infrastructure (MLIR). Finally, we identify problems that limit the scalability of existing DSEs, which we name the “design space merging problem.” We address this issue by employing a Random Forest classifier that can successfully decrease the number of invalid design points without invoking the HLS compiler as a validation tool. We evaluated our DSE engine against the ScaleHLS DSE, outperforming it by a maximum of 59X. We additionally demonstrate the scalability of our design by applying our DSE to large-scale HLS designs, achieving a maximum speedup of 12x for the benchmarks in the MachSuite and Rodinia set.

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Design Space Exploration for Scheduling and Allocation in High Level Synthesis of Datapaths

Cited by 2 publications

References 24 publications

N-PIR: A Neighborhood-Based Pareto Iterative Refinement Approach for High-Level Synthesis

N-PIR: A Neighborhood-Based Pareto Iterative Refinement Approach for High-Level Synthesis

AutoScaleDSE: A Scalable Design Space Exploration Engine for High-Level Synthesis

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