Deep learning algorithms are becoming an essential component of many artificial intelligence (AI) driven applications, many of which run on resource-constrained and energy-constrained systems. For efficient deployment of these algorithms, although different techniques for the compression of neural networks models are proposed, neural pruning is one of the fast and effective methods which can provide a high compression gain with minimal cost. To harness enhanced performance gain with respect to model complexity, we propose a novel neural network pruning approach utilizing Hessian-vector products that approximates crucial curvature information in the loss function which significantly reduces the computation demands. By employing a power iteration method, our algorithm effectively identifies and preserves the essential information, ensuring a balanced trade-off between model accuracy and computational efficiency. Herein, we introduce CAMP-HiVe, a cyclic pair merging based pruning with Hessian Vector approximation by iteratively consolidating weight pairs, combining significant and less significant weights, thus effectively streamlining the model while preserving its performance. This dynamic, adaptive framework allows for real-time adjustment of weight significance, ensuring that only the most critical parameters are retained. Our experimental results demonstrate that our proposed method achieves significant reductions in computational requirements while maintaining high performance across different neural network architectures, e.g., ResNet18, ResNet56, and MobileNetv2 on standard benchmark datasets, e.g., CIFAR10, CIFAR-100, and ImageNet, and it outperforms the existing state-of-the-art neural pruning methods.

With the advancement of industrial manufacturing and an increase in introduction of robots in the workspace, the need of safe operation, communication and information sharing is paramount. The work presented here focuses on cyber-physical system integration through Digital Twin (DT) technology. Our novel DT architecture is based on a model-free Neuro-Adaptive controller (NAC), and an edge-computing scheme for scene monitoring. The NAC can account for varying robot dynamics in both real and virtual environments, and allows for the DT system to expand the realm of cyber-physical integration without expensive model tuning. The edge-computing device introduced in our architecture, observes the robot's workspace from a distance with a wider field of view. This wide viewpoint, enhances the detection and mitigation of any obstacles entering the robot's workspace during operation. We experimentally evaluated the performance of our proposed architecture by introducing dynamic obstacles during a pick-and-place task that both the physical robot and its digital twin had to avoid. Results show that the proposed DT architecture successfully integrates the novel controller and edge-computing elements and successfully performs the given navigation task. The results also show that NAC outperforms a PD controller with more than 70% improvement in joint tracking error between the physical and virtual robots. It was observed that the latency experienced while using NAC is about 48 % lower than when Proportional-Derivative (PD) controller was operational.

Digital Twin technology is being envisioned to be an integral part of the industrial evolution in modern generation. With the rapid advancement in the Internet-of-Things (IoT) technology and increasing trend of automation, integration between the virtual and the physical world is now realizable to produce practical digital twins. However, the existing definitions of digital twin is incomplete and sometimes ambiguous. Herein, we conduct historical review and analyze the modern generic view of digital twin to create its new extended definition. We also review and discuss the existing work in digital twin in safety-critical robotics applications. Especially, the usage of digital twin in industrial applications necessitates autonomous and remote operations due to environmental challenges. However, the uncertainties in the environment may need close monitoring and quick adaptation of the robots which need to be safety-proof and cost effective. We demonstrate a case study on developing a framework for safety-critical robotic arm applications and present the system performance to show its advantages, and discuss the challenges and scopes ahead.