Abstract
Neighborhood image processing operations on Field Programmable Gate Array (FPGA) are considered as memory intensive operations. A large memory bandwidth is required to transfer the required pixel data from external memory to the processing unit. On-chip image buffers are employed to reduce this data transfer rate. Conventional image buffers, implemented either by using FPGA logic resources or embedded memories are resource inefficient. They exhaust the limited FPGA resources quickly. Consequently, hardware implementation of neighborhood operations becomes expensive, and integrating them in resource constrained devices becomes unfeasible. This paper presents a resource efficient FPGA based on-chip buffer architecture. The proposed architecture utilizes full capacity of a single Xilinx Block-RAM (BRAM36 primitive) for storing multiple rows of input image. To get multiple pixels/clock in a user defined scan order, an efficient duty-cycle based memory accessing technique is coupled with a customized addressing circuitry. This accessing technique exploits switching capabilities of BRAM to read 4 pixels in a single clock cycle without degrading system frequency. The addressing circuitry provides multiple pixels/clock in any user defined scan order to implement a wide range of neighborhood operations. With the saving of 83% BRAM resources, the buffer architecture operates at 278 MHz on Xilinx Artix-7 FPGA with an efficiency of 1.3 clock/pixel. It is thus capable to fulfill real time image processing requirements for HD image resolution ( $1080\times 1920$ ) @103 fps.
Highlights
Recent technological advancements open new avenues in mobile and portable imaging devices
It is comprised of one block RAM (BRAM) [27], Address Generator Module (AGM), Clocking Circuitry (CC), Clock Selector (CLK SEL) and a set of eight Registers (R0-R7)
The design was implemented on a low power Xilinx Artix7 (XC7A35T) Field Programmable Gate Array (FPGA) device using Xilinx Integrated Software Environment (ISE) 14.6
Summary
Recent technological advancements open new avenues in mobile and portable imaging devices These devices are progressively becoming more compact with emphasis on autonomous power supply [1]. In such resource (area, power) constrained devices, it becomes very challenging to integrate various computationally intensive low-level image processing units, which are indispensable for improving the quality of captured images. The neighborhood operations are widely used low level image processing operations, to enhance the visual quality of captured images [2], [3]. A (N×N) window processes (N×N) neighborhood input pixels per output pixel, leading to high pixel data transfer rate of N2 pixels, between external memory and processor unit that eventually requires a large memory bandwidth
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