Investigating Artificial Intelligence Systems Through the Use of Constrained Deep Neural Networks

Deep neural networks have significantly advanced the field of image and text categorization, pushing the boundaries of machine learning. However, designing efficient neural network architectures remains a challenge, often requiring complex and costly methods to find optimal configurations. This paper introduces a novel approach to architectural design through a restricted search method, focusing on creating networks that are both cost-effective and fast, suitable for AI systems with strict memory and time limitations, particularly in near-sensor applications. Neural networks now surpass traditional machine learning techniques in various computational perception tasks. Despite their success, deploying these advanced models on mobile and IoT devices is computationally challenging, leading to reliance on cloud-based solutions. Such dependence increases communication costs and potential system inoperability during connectivity outages. Our method addresses these issues, offering a viable solution for efficient, local deployment of advanced neural networks in resource-constrained environments. We propose a conceptual framework that leverages a Deep neural networks (DNN) approach to decide whether data should be processed locally or sent to the cloud, optimizing both computational resources and performance. Our findings suggest that this method requires sending only 52% of test data to the server, achieving an overall system accuracy of 48%. This significantly enhances the efficiency of client-server models and supports the implementation of AI capabilities on local devices. By employing a strategic search for computational models based on content extraction, we improve the efficiency and speed of AI operations. Our experiments demonstrate the practicality and effectiveness of this approach, which has also been tested on actual hardware, offering a promising direction for enhancing AI applications in resource-constrained environments.