Gradient-Based Compression and Approximate Computing for Deep Network Optimization in Multimedia Data Processing

Existing methods of compressed multimedia data processing in deep networks are constrained by inherent trade-offs such as low reconstruction accuracy, compression ratio, and high computational latency. With computational intensive tasks, the performance further deteriorates owing to high energy requirements and processing delays in real-time, large-scale multimedia applications. This work presents a deep learning framework that integrates gradient-based compression with approximate computing to optimize the in-network processing of multimedia data. Hyperparameter tuning is employed to systematically adjust bit-width, model size, and network depth, enabling fine-grained control over compression and computational efficiency. The proposed approach makes use of adaptive convolutional layers and dynamic learning rates for local gradient residue compression with the aim to improve the exploitation of low-rank structures and data sparsity. Extensive simulations are performed on publicly available datasets, including CIFAR-10, CIFAR-100, and MNIST, to validated the performance of the proposed method. Results demonstrate the effectiveness of the proposed method as it outperforms a set of state-of-the-art approaches achieving a classification accuracy of 99.09%, with almost real-time processing of the multimedia data.