Abstract
In recent years we have witnessed the emergence of the FPGA in many high-performance systems. This is due to FPGA’s high reconfigurability and improved user-friendly programming environment. OpenCL, supported by major FPGA vendors, is a high-level programming platform that liberates hardware developers from having to deal with the complex and error-prone HDL development. While OpenCL exposes a GPU-like programming model, which is well-suited for compute-intensive tasks, in many state-of-art systems that deploy FPGA, we observe that the workloads are streaming-like, which is communication-intensive. This mismatch leads to low throughput and high end-to-end latency. In this paper, we propose ST-Accel, a new high-level programming platform for streaming applications on FPGA. It has the following advantages: (i) ST-Accel adopts the multiprocessing programming model to capture the inherent pipeline-level parallelism of streaming applications while reducing the end-to-end latency. (ii) A message-passing-based host/FPGA communication model is used to avoid the coherency issue of shared memory, thus enabling host/FPGA communication during kernel execution. (iii) ST-Accel provides a high-level abstraction for I/O devices to support direct I/O device access that eliminates the overhead of host CPU and reduces the I/O latency. (iv) ST-Accel enables the decoupled access/execute architecture to maximize the utilization of I/O devices. (v) The host/FPGA communication interface is redesigned to cater to the demands of both latency-critical and throughput-critical scenarios. The experimental results on the Amazon AWS cloud and local machine show that ST-Accel can achieve 1.6X-166X throughput and 1/3 latency for typical streaming workloads when compared to OpenCL.
Peipei Zhou
Assistant Professor of Engineering
My research interests include Customized Computer Architecture and Programming Abstraction for Health & AI Applications