TARO: Automatic Optimization for Free-Running Kernels in FPGA High-Level Synthesis

Streaming applications have become one of the key application domains for high-level synthesis (HLS) tools. For a streaming application, there is a potential to simplify the control logic by regulating each task with a stream of input and output data. This is called free-running optimization. But it is difficult to understand when such optimization can be applied without changing the functionality of the original design. Moreover, it takes a large effort to manually apply the optimization across legacy codes. In this article, we present the TARO framework which automatically applies the free-running optimization on HLS-based streaming applications. TARO simplifies the control logic without degrading the clock frequency or the performance. Experiments on Alveo U250 shows that we can obtain an average of 16% LUT and 45% FF reduction for streaming-based systolic array designs.