Network-on-Chip (NoC) has been proposed as a promising solution to overcome the communication challenges of System-on-Chip (SoC) design in nanoscale technologies. With the advancement in the nanoscale technology, the integration density of Intellectual Property (IP) cores in a single chip have increased, leading to heat dissipation, which in turn makes the system unreliable. Therefore, efficient fault-tolerant methods are necessary at different levels to improve overall system performance and make the system to operate normally. This article presents a flexible spare core placement technique for mesh-based NoC by taking several benchmark applications into consideration. An Integer Linear Programming (ILP)-based solution has been proposed for the spare core placement problem. Also, Particle Swarm Optimisation (PSO)-based meta-heuristic has been proposed for the same. Experiments have been performed by taking several application benchmarks reported in the literature and the applications generated using the TGFF tool. Comparisons have been carried out using our approach and the approach followed in the literature (i) by varying the network size with fixed fault percentage in the network, and (ii) by fixing the network size while varying the percentage of faults in the network. We have also compared the overall communication cost and CPU runtime between ILP and PSO approaches. The results show significant reductions in the overall communication cost, average network latency, and network power consumption across all the cases using our approach over the approaches reported in the literature.
Hand gestures based sign language digits have several contactless applications. Applications include communication for impaired people, such as elderly and disabled people, health-care applications, automotive user interfaces, and security and surveillance. This work presents the design and implementation of a complete end-to-end deep learning based edge computing system that can verify a user contactlessly using ‘authentication code’. The ‘authentication code’ is an ‘n’ digit numeric code and the digits are hand gestures of sign language digits. We propose a memory-efficient deep learning model to classify the hand gestures of the sign language digits. The proposed deep learning model is based on the bottleneck module which is inspired by the deep residual networks. The model achieves classification accuracy of 99.1% on the publicly available sign language digits dataset. The model is deployed on a Raspberry pi 4 Model B edge computing system to serve as an edge device for user verification. The edge computing system consists of two steps, it first takes input from the camera attached to it in real-time and stores it in the buffer. In the second step, the model classifies the digit with the inference rate of 280 ms, by taking the first image in the buffer as input.
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