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A novel lightweight CNN-based error-reduced carry prediction approximate full adder design for multimedia applications

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Abstract

Approximation approaches have been developed for very large-scale integration architecture to limit power consumption and increase effective throughput. The full adder (FA) and block-based designs are the approximate adder designs widely used in the existing techniques to accelerate the additions by partitioning a long carry propagation chain. However, these techniques require more power and area than the standard FA-based techniques. To overcome this issue, this paper presents an error-reduced carry prediction approximate full adder (ERCPAA) technique to achieve faster additions and performance by adding a constant truncation mechanism with an error reduction strategy. Deep learning and neural network technology had made great progress in recent years for field programmable gate array implementation and real-time object detection. It is essential to maintain high accuracy rates in image processing applications, as any errors introduced during the processing can significantly impact the quality of the output image and impact the hardware implementation. Therefore, we propose an ERCPAA that utilizes a lightweight convolutional neural network (ERCPAA–lightweight CNN) to fit well in a fixed-point CNN accelerator architecture. A lightweight CNN that is both thin (with fewer feature maps per layer) and deep (four layers) with just one fully connected hidden layer can enable faster training while achieving higher accuracy. In addition, in a few positions of the higher-order bits of the incorrect part, a full adder (FA) cell is used to simplify the traditional one-bit FA cell, creating an approximate summation and carry. The proposed method is verified by randomly selecting specific images from the Fruit 360, ImageNet, and the Caltech 256 Image Dataset. The proposed model offers an normalized mean error distance, error rate, and mean relative error distance values of 0.154, 6.55%, and 4.6E-4. The recognition accuracy of the proposed model in the ImageNet database is 96% with an execution time of 1420 µs.

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Data availability

The data that supports the findings of this study is available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Anna University, Chennai, Tamil Nadu, India

    R. Nishanth

  2. Department of Electronics and Communication Engineering, St. Xavier’s Catholic College of Engineering, Nagarcoil, Tamil Nadu, India

    C. Helen Sulochana

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  1. R. Nishanth
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Nishanth, R., Sulochana, C.H. A novel lightweight CNN-based error-reduced carry prediction approximate full adder design for multimedia applications. Neural Comput & Applic 36, 6421–6440 (2024). https://doi.org/10.1007/s00521-023-09316-z

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