2024/06/08 by Seyed Erfan Fatemieh, Fatemieh, Seyed Erfan, Mohammad Reza Reshadinezhad +1 · 1 citation
Computer Science · Engineering · #Advanced Memory and Neural Computing #CCD and CMOS Imaging Sensors #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Neural Networks and Applications #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2406.05525
openalex publication_date 2024/06/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Researchers and designers are facing problems with memory and power walls, considering the pervasiveness of Von-Neumann architecture in the design of processors and the problems caused by reducing the dimensions of deep sub-micron transistors. Memristive Approximate Computing (AC) and In-Memory Processing (IMP) can be promising solutions to these problems. We have tried to solve the power and memory wall problems by presenting the implementation algorithm of four memristive approximate full adders applying the Material Implication (IMPLY) method. The proposed circuits reduce the number of computational steps by up to 40% compared to the state-of-the-art. The energy consumption of the proposed circuits improves over the previous exact ones by 49%-75% and over the approximate full adders by up to 41%. Multiple error evaluation criteria evaluate the computational accuracy of the proposed approximate full adders in three scenarios in the 8-bit approximate adder structure. The proposed approximate full adders are evaluated in three image processing applications in three scenarios. The results of application-level simulation indicate that the four proposed circuits can be applied in all three scenarios, considering the acceptable image quality metrics of the output images.