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Journal ArticleDOI

High Performance Static Segment On-Chip Memory for Image Processing Applications

TLDR
A design of high speed, area, and energy efficient Static Segment On-Chip (SSOC) memory for error-tolerant applications and the proposed m-bit static segmentation algorithm is implemented and verified in Single Port Static Random Access Memory (SP SRAM) architecture.
Abstract
The performance of the processor core depends on the configuration parameters and utilization of on-chip memory in multimedia applications such as image, video and audio processing. The design of the on-chip memory architecture is critical for power and area efficient design without compromising quality in data-intensive computing applications. This paper proposes a design of high speed, area, and energy efficient Static Segment On-Chip (SSOC) memory for error-tolerant applications. In this static segment method, n-bit data array is reduced by m-bit data array for significant value of input data to achieve balanced design metrics at the cost of accuracy. The proposed m-bit static segmentation algorithm is implemented and verified in Single Port Static Random Access Memory (SP SRAM) architecture for the approximate computing applications. From the overall simulation results, the proposed 4-bit SSOC SP SRAM design provides 49.02% area savings, 50.62% power reduction and 16.92% speed improvement at the cost of 0.64% Peak Signal to Noise Ratio (PSNR) and exhibits same visual quality in comparison with the existing 8-bit conventional on-chip SP SRAM design in the image processing applications.

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Citations
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Journal ArticleDOI

WIRD: An Efficiency Migration Scheme in Hybrid DRAM and PCM Main Memory for Image Processing Applications

TL;DR: A novel page migration scheme for hybrid DRAM and PCM memory architecture called write frequency with inter-reference distance (WIRD) is presented, which minimizes the unnecessary page migrations which make most of the write hot pages absorbed by DRAM efficiently.
Journal ArticleDOI

High Performance Approximate Memories for Image Processing Applications

TL;DR: The proposed HASP SRAM provides 14.99% less power consumption and thirteen numbers of logic elements savings in the resource utilization than the existing conventional SP SRAM, and the proposed HASBDP SRAMs outperform than the conventional TDP and sub-bank DP SRAM approaches.
References
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TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Journal ArticleDOI

EnerJ: approximate data types for safe and general low-power computation

TL;DR: EnerJ is developed, an extension to Java that adds approximate data types and a hardware architecture that offers explicit approximate storage and computation and allows a programmer to control explicitly how information flows from approximate data to precise data.
Proceedings ArticleDOI

Neural Acceleration for General-Purpose Approximate Programs

TL;DR: A programming model is defined that allows programmers to identify approximable code regions -- code that can produce imprecise but acceptable results and is faster and more energy efficient than executing the original code.
Proceedings ArticleDOI

Managing performance vs. accuracy trade-offs with loop perforation

TL;DR: The results indicate that, for a range of applications, this approach typically delivers performance increases of over a factor of two (and up to a factors of seven) while changing the result that the application produces by less than 10%.
Book

Design for Embedded Image Processing on FPGAs

TL;DR: Design for Embedded Image Processing on FPGAs is ideal for researchers and engineers in the vision or image processing industry, who are looking at smart sensors, machine vision, and robotic vision, as well as FPGA developers and application engineers.
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