Affiliation:
1. Computer Systems Group, International Institute of Information Technology Hyderabad, India
Abstract
The exponential performance growth guaranteed by Moore’s law has started to taper in recent years. At the same time, emerging applications like image processing demand heavy computational performance. These factors inevitably lead to the emergence of domain-specific accelerators (DSA) to fill the performance void left by conventional architectures. FPGAs are rapidly evolving towards becoming an alternative to custom ASICs for designing DSAs because of their low power consumption and a higher degree of parallelism. DSA design on FPGAs requires careful calibration of the FPGA compute and memory resources towards achieving optimal throughput.
Hardware Descriptive Languages (HDL) like Verilog have been traditionally used to design FPGA hardware. HDLs are not geared towards any domain, and the user has to put in much effort to describe the hardware at the register transfer level. Domain Specific Languages (DSLs) and compilers have been recently used to weave together handwritten HDLs templates targeting a specific domain. Recent efforts have designed DSAs with image-processing DSLs targeting FPGAs. Image computations in the DSL are lowered to pre-existing templates or lower-level languages like HLS-C. This approach requires expensive FPGA re-flashing for every new workload. In contrast to this fixed-function hardware approach, overlays are gaining traction.
Overlays
are DSAs resembling a processor, which is synthesized and flashed on the FPGA once but is flexible enough to process a broad class of computations through soft reconfiguration. Less work has been reported in the context of image processing overlays. Image processing algorithms vary in size and shape, ranging from simple blurring operations to complex pyramid systems. The primary challenge in designing an image-processing overlay is maintaining flexibility in mapping different algorithms.
This paper proposes a DSL-based overlay accelerator called
FlowPix
for image processing applications. The DSL programs are expressed as pipelines, with each stage representing a computational step in the overall algorithm. We implement 15 image-processing benchmarks using FlowPix on a Virtex-7-690t FPGA. The benchmarks range from simple blur operations to complex pipelines like Lucas-Kande optical flow. We compare FlowPix against existing DSL-to-FPGA frameworks like Hetero-Halide and Vitis Vision library that generate fixed-function hardware. On most benchmarks, we see up to 25% degradation in latency with approximately a 1.7x to 2x increase in the FPGA LUT consumption. Our ability to execute any benchmark without incurring the high costs of hardware synthesis, place-and-route, and FPGA re-flashing justifies the slight performance loss and increased resource consumption that we experience. FlowPix achieves an average frame rate of 170 FPS on HD frames of 1920 × 1080 pixels in the implemented benchmarks.
Publisher
Association for Computing Machinery (ACM)
Subject
Hardware and Architecture,Information Systems,Software
Reference41 articles.
1. Puya Amiri, Arsène Pérard-Gayot, Richard Membarth, Philipp Slusallek, Roland Leißa, and Sebastian Hack. 2021. FLOWER: A comprehensive dataflow compiler for high-level synthesis. In 2021 International Conference on Field-Programmable Technology (ICFPT). IEEE, 1–9.
2. FlexGrip: A soft GPGPU for FPGAs
3. Chisel
4. FPGA programming for the masses
5. The Future of FPGA Acceleration in Datacenters and the Cloud