Input-Based Dynamic Reconfiguration of Approximate Arithmetic Units for Video Encoding

Abstract:

The field of approximate computing has received significant attention from the research community in the past few years, especially in the context of various signal processing applications. Image and video compression algorithms, such as JPEG, MPEG, and so on, are particularly attractive candidates for approximate computing, since they are tolerant of computing imprecision due to human imperceptibility, which can be exploited to realize highly power-efficient implementations of these algorithms. However, existing approximate architectures typically fix the level of hardware approximation statically and are not adaptive to input data. For example, if a fixed approximate hardware configuration is used for an MPEG encoder (i.e., a fixed level of approximation), the output quality varies greatly for different input videos. This paper addresses this issue by proposing a reconfigurable approximate architecture for MPEG encoders that optimizes power consumption with the goal of maintaining a particular Peak Signal-to-Noise Ratio (PSNR) threshold for any video. We propose two heuristics for automatically tuning the approximation degree of the RABs in these two modules during runtime based on the characteristics of each individual video. The proposed architecture of this paper analysis the logic size, area and power consumption using Xilinx 14.2.

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