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FPGAs are an excellent target for real-time video processing as they provide large amounts of low-level parallelism, low latency, and high bandwidth. However, creating real-time video processing systems on an FPGA is tedious and requires significant effort and low-level digital design skills. This paper presents a technique for creating complex real-time video processing pipelines relatively quickly and easily using partial reconfiguration (PR). A static FPGA system is created that provides a template for a variety of partially reconfigurable video processing cores. A library of video filters has been created that can be inserted into the template regions. At run-time, the user can select the topology of video cores and customize these cores to create complex and unique video pipelines without any understanding of low-level FPGA details. This paper demonstrates this technique with a library of 11 partial reconfigurable regions and 16 video processing cores operating on the Xilinx PYNQ system.
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Sprocket: A Serverless Video Processing Framework
Sprocket is a highly configurable, stage-based, scalable, serverless video processing framework that exploits intra-video parallelism to achieve low latency. Sprocket enables developers to program a series of operations over video content in a modular, extensible manner. Programmers implement custom operations, ranging from simple video transformations to more complex computer vision tasks, in a simple pipeline specification language to construct custom video processing pipelines. Sprocket then handles the underlying access, encoding and decoding, and processing of video and image content across operations in a highly parallel manner. In this paper we describe the design and implementation of the Sprocket system on the AWS Lambda serverless cloud infrastructure, and evaluate Sprocket under a variety of conditions to show that it delivers its performance goals of high parallelism, low latency, and low cost (10s of seconds to process a 3,600 second video 1000-way parallel for less than $3).
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- PAR ID:
- 10098946
- Date Published:
- Journal Name:
- Proceedings of the ACM Symposium on Cloud Computing (SoCC ’18)
- Page Range / eLocation ID:
- 263 to 274
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3267809.3267815
