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DevConf.US 2022 is the 5th annual, free, Red Hat sponsored technology conference for community project and professional contributors to Free and Open Source technologies coming to Boston this August!!
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Thursday, August 18 • 13:00 - 13:25
Open Hardware Initiative Series: Reinforcement Learning based HLS Compiler Tuning

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Despite the proliferation of Field Programmable Gate Arrays (FPGAs) in both the cloud and edge, the complexity of hardware development has limited its accessibility to developers. High Level Synthesis (HLS) offers a possible solution by automatically compiling CPU codes to custom circuits, but currently delivers far lower hardware quality than circuits written using Hardware Description Languages (HDLs). This is because the standard set of code optimizations used by CPU compilers, such as LLVM, are not suited for an FPGA backend. In order to bridge the gap between hand tuned and automatically generated hardware, it is thus important to determine the optimal pass ordering for HLS compilations, which could vary substantially across different workloads. Since there are dozens of possible passes and virtually infinite combinations of them, manually discovering the optimal pass ordering is not practical. Instead, we will use reinforcement learning to automatically learn how to best optimize a given workload (or a class of workloads) for FPGAs. Specifically, we investigate the use of reinforcement learning to discover the optimal set of optimization passes (including their ordering and frequency of application) for LLVM based HLS - a technique for compiler tuning that has been shown to be effective for CPU workloads. In this talk, we will present the results of our experiments aimed at exploring how HLS compiler tuning is impacted by different strategies in reinforcement. This includes, but is not limited to: i) selection of features, ii) methods for reward calculation, iii) selection of agent, iv) action space and v) training parameters. Our goal will be to identify strategies which converge to the best possible solution, take the least amount of time for doing so, and provide results which can be applied to a class of workloads instead of individual ones (to avoid retraining the model).


Thursday August 18, 2022 13:00 - 13:25 EDT
East Balcony

Attendees (9)