Optimized VLSI Architecture of HEVC Fractional Pixel Interpolators with Approximate Computing

Summary Statistical analysis of High Efficiency Video Coding (HEVC) encoder reveals that in the motion compensation block, the interpolation filter consumes more than 30% in the encoder time in comparison with other blocks. In this paper, we start with an optimized hardware implementation of the interpolation filter on field‐programmable gate array (FPGA) based on Xilinx setup environment. In a second step, a Hardware/Software (HW/SW) co‐design implementation of HM16.7 encoder is performed on Zynq MPSoC platform to evaluate the proposed interpolation filter IP in terms of total encoder run‐time, taking advantages of both processing units (quad‐core ARM Cortex TM‐A53 processor and Programmable Logic FPGA component) available on the Zynq MPSoC. The proposed architecture of luma and chroma filters was simulated and synthesized on Xilinx XCZU7EV‐2FFVC1156 FPGA at 250‐MHz clock frequency. The synthesis results present an optimized power consumption of 3.308 mW for higher resolutions (2560 × 1600 and 1920 × 1080) at 50 fps with the use of just 1% of the FPGA resources. The experimental results of the co‐design implementation of HEVC encoder present a speedup of 2 times (41% in PeopleOnStreet sequence) in terms of processing time compared to the software alone implementation, with a an increase of 0.51% of bit rate and a very small degradation of peak signal‐to‐noise ratio (PSNR) (0.01%).

show abstract

Co‐design implementation of High Efficiency Video Coding standard encoder on Zynq MPSoC

Touzani

Mansouri

Errahimi

et al. 2021

Circuit Theory & Apps

View full text Add to dashboard Cite

show abstract

Approximation Opportunities in Edge Computing Hardware: A Systematic Literature Review

Damsgaard¹,

Ometov²,

Nurmi

2023

ACM Comput. Surv.

View full text Add to dashboard Cite

With the increasing popularity of the Internet of Things and massive Machine Type Communication technologies, the number of connected devices is rising. However, while enabling valuable effects to our lives, bandwidth and latency constraints challenge Cloud processing of their associated data amounts. A promising solution to these challenges is the combination of Edge and approximate computing techniques that allows for data processing nearer to the user. This paper aims to survey the potential benefits of these paradigms’ intersection. We provide a state-of-the-art review of circuit-level and architecture-level hardware techniques and popular applications. We also outline essential future research directions.

show abstract

Designing Energy-Efficient Approximate Multipliers

Perri¹,

Spagnolo²,

Frustaci³

et al. 2022

JLPEA

View full text Add to dashboard Cite

This paper proposes a novel approach suitable to design energy-efficient approximate multipliers using both ASIC and FPGAs. The new strategy harnesses specific encoding logics based on bit significance and computes the approximate product performing accurate sub-multiplications by applying an unconventional approach instead of using approximate computational modules implementing traditional static or dynamic bit-truncation approaches. The proposed platform-independent architecture exhibits an energy saving of up to 80% over the accurate counterparts and significantly better behavior in terms of accuracy loss with respect to competitor approximate architectures. When employed in 2D digital filters and edge detectors, the novel approximate multipliers lead to an energy consumption up to ~82% lower than the accurate counterparts, which is up to ~2 times higher than that obtained by state-of-the-art competitors.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Optimized VLSI Architecture of HEVC Fractional Pixel Interpolators with Approximate Computing

Cited by 3 publications

References 19 publications

Co‐design implementation of High Efficiency Video Coding standard encoder on Zynq MPSoC

Co‐design implementation of High Efficiency Video Coding standard encoder on Zynq MPSoC

Approximation Opportunities in Edge Computing Hardware: A Systematic Literature Review

Designing Energy-Efficient Approximate Multipliers

Contact Info

Product

Resources

About