FPGA based Video processing acceleration

Photo courtesy: Zhiru Zhang

Video processing is a computationally intensive task that requires high performance and low latency. One way to achieve this is by using FPGA (Field Programmable Gate Array) based acceleration. 

In this blog post, we will introduce some of the basic concepts and benefits of FPGA based video processing acceleration, and provide some examples of how it can be used in various scenarios.

FPGA vs CPU/GPU

CPU (Central Processing Unit) and GPU (Graphics Processing Unit) are the most common types of processors used for video processing. CPU is a general-purpose processor that can execute a variety of instructions, while GPU is a specialized processor that can perform parallel operations on large arrays of data. Both CPU and GPU have fixed architectures that are designed for a broad range of applications, and rely on software to implement the video processing algorithms.

FPGA, on the other hand, is a programmable hardware device that can implement any logic function that can be described by a hardware description language (HDL). FPGA has a matrix of logic blocks that can be configured to perform different operations, and interconnects that can route data between them. FPGA can also have dedicated hardware blocks for specific functions, such as memory, arithmetic, or I/O. FPGA can be customized to match the exact requirements of the video processing application, and optimize the performance, power consumption, and resource utilization.

Some of the benefits of FPGA based video processing acceleration are:

1. Parallelism: FPGA can exploit the inherent parallelism in video processing by implementing multiple processing units that can operate concurrently on different parts of the video data. This can reduce the latency and increase the throughput of the system.
2. Flexibility: FPGA can adapt to different video formats, resolutions, frame rates, and algorithms by reconfiguring the logic blocks and interconnects. This can enable dynamic switching between different modes of operation, and support new features or standards without changing the hardware.
3. Energy efficiency: FPGA can achieve lower power consumption than CPU or GPU by eliminating unnecessary components and overheads, such as instruction fetch, decode, cache, or operating system. FPGA can also adjust the clock frequency and voltage according to the workload, and turn off unused logic blocks or interconnects to save energy.

Examples of FPGA based video processing acceleration:

FPGA based video processing acceleration can be applied in various domains, such as:

1. Video encoding/decoding: FPGA can accelerate the compression and decompression of video data using various codecs, such as H.264, HEVC, AV1, etc. FPGA can implement parallel encoding/decoding engines that can handle multiple video streams simultaneously, and support high resolutions (e.g., 4K or 8K) and frame rates (e.g., 60 fps or 120 fps).
2. Video analytics: FPGA can accelerate the extraction of useful information from video data using various techniques, such as object detection, face recognition, scene classification, etc. FPGA can implement parallel analytics pipelines that can process multiple video streams in real time, and support complex algorithms (e.g., deep neural networks) with high accuracy and low latency.
3. Video enhancement: FPGA can accelerate the improvement of video quality using various methods, such as noise reduction, deinterlacing, scaling, sharpening, etc. FPGA can implement parallel enhancement modules that can process multiple video streams with high quality and low latency.

Conclusion

FPGA based video processing acceleration is a promising technology that can offer high performance and low latency for various video processing applications. FPGA can leverage its parallelism, flexibility, and energy efficiency to outperform CPU or GPU based solutions. FPGA can also enable new features or standards by reconfiguring its hardware without changing its physical device.