Enhanced CNN Performance without Retraining Via Weight Approximation and Data Reuse

This paper introduces an efficient CNN algorithm to address key limitations in Deep Neural Networks (DNNs) used for image recognition, focusing particularly on model size and retraining time. Traditional methods often require significant training durations; however, applying approximation techniques during retraining can exacerbate these time demands. We present an approach that enhances approximation techniques while eliminating the need for model retraining, thus enabling DNN compression with minimal accuracy loss. The proposed method integrates three core strategies: weight arrangement, approximation, and data reuse. The DNN weights are initially arranged in ascending order to optimize subsequent operations. During inference, the approximation is applied to reduce the model size and minimize computational complexity by reducing the number of operations required for each multiply-accumulate (MAC) unit. Then, the original weights are replaced with the approximated values, enabling the reuse of computations and data across different sets of weights. As a result, the method significantly reduces memory access, computational demands, and energy consumption. Experimental results on the CIFAR-10 and TinyImageNet datasets demonstrate that our method achieves a model reduction rate of approximately 198.6× while maintaining a minimal loss in accuracy. The proposed technique bypasses the need for retraining, offering a practical solution to the growing complexity of DNN models in modern applications.