Savior: A Reliable Fault Resilient Router Architecture for Network-on-Chip

[EN] The router plays an important role in communication among different processing cores in on-chip networks. Technology scaling on one hand has enabled the designers to integrate multiple processing components on a single chip; on the other hand, it becomes the reason for faults. A generic router consists of the buffers and pipeline stages. A single fault may result in an undesirable situation of degraded performance or a whole chip may stop working. Therefore, it is necessary to provide permanent fault tolerance to all the components of the router. In this paper, we propose a mechanism that can tolerate permanent faults that occur in the router. We exploit the fault-tolerant techniques of resource sharing and paring between components for the input port unit and routing computation (RC) unit, the resource borrowing for virtual channel allocator (VA) and multiple paths for switch allocator (SA) and crossbar (XB). The experimental results and analysis show that the proposed mechanism enhances the reliability of the router architecture towards permanent faults at the cost of 29% area overhead. The proposed router architecture achieves the highest Silicon Protection Factor (SPF) metric, which is 24.4 as compared to the state-of-the-art fault-tolerant architectures. It incurs an increase in latency for SPLASH2 and PARSEC benchmark traffics, which is minimal as compared to the baseline router.This work was supported by the Spanish 'Ministerio de Ciencia Innovacion y Universidades' and FEDER program in the framework of the 'Proyectos de I+D d Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I+D+i, Subprograma Estatal de Generacion de Conocimiento' (ref: PGC2018-095747-B-I00).Hussain, A.; Irfan, M.; Baloch, NK.; Draz, U.; Ali, T.; Glowacz, A.; Dunai, L.... (2020). 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IWO-IGA—A Hybrid Whale Optimization Algorithm Featuring Improved Genetic Characteristics for Mapping Real-Time Applications onto 2D Network on Chip

Network on Chip (NoC) has emerged as a potential substitute for the communication model in modern computer systems with extensive integration. Among the numerous design challenges, application mapping on the NoC system poses one of the most complex and demanding optimization problems. In this research, we propose a hybrid improved whale optimization algorithm with enhanced genetic properties (IWOA-IGA) to optimally map real-time applications onto the 2D NoC Platform. The IWOA-IGA is a novel approach combining an improved whale optimization algorithm with the ability of a refined genetic algorithm to optimally map application tasks. A comprehensive comparison is performed between the proposed method and other state-of-the-art algorithms through rigorous analysis. The evaluation consists of real-time applications, benchmarks, and a collection of arbitrarily scaled and procedurally generated large-task graphs. The proposed IWOA-IGA indicates an average improvement in power reduction, improved energy consumption, and latency over state-of-the-art algorithms. Performance based on the Convergence Factor, which assesses the algorithm’s efficiency in achieving better convergence after running for a specific number of iterations over other efficiently developed techniques, is introduced in this research work. These results demonstrate the algorithm’s superior convergence performance when applied to real-world and synthetic task graphs. Our research findings spotlight the superior performance of hybrid improved whale optimization integrated with enhanced GA features, emphasizing its potential for application mapping in NoC-based systems

An Optimized Nature-Inspired Metaheuristic Algorithm for Application Mapping in 2D-NoC

Mapping application task graphs on intellectual property (IP) cores into network-on-chip (NoC) is a non-deterministic polynomial-time hard problem. The evolution of network performance mainly depends on an effective and efficient mapping technique and the optimization of performance and cost metrics. These metrics mainly include power, reliability, area, thermal distribution and delay. A state-of-the-art mapping technique for NoC is introduced with the name of sailfish optimization algorithm (SFOA). The proposed algorithm minimizes the power dissipation of NoC via an empirical base applying a shared k-nearest neighbor clustering approach, and it gives quicker mapping over six considered standard benchmarks. The experimental results indicate that the proposed techniques outperform other existing nature-inspired metaheuristic approaches, especially in large application task graphs

Fault-Tolerant Network-On-Chip Router Architecture Design for Heterogeneous Computing Systems in the Context of Internet of Things

Network-on-chip (NoC) architectures have become a popular communication platform for heterogeneous computing systems owing to their scalability and high performance. Aggressive technology scaling makes these architectures prone to both permanent and transient faults. This study focuses on the tolerance of a NoC router to permanent faults. A permanent fault in a NoC router severely impacts the performance of the entire network. Thus, it is necessary to incorporate component-level protection techniques in a router. In the proposed scheme, the input port utilizes a bypass path, virtual channel (VC) queuing, and VC closing strategies. Moreover, the routing computation stage utilizes spatial redundancy and double routing strategies, and the VC allocation stage utilizes spatial redundancy. The switch allocation stage utilizes run-time arbiter selection. The crossbar stage utilizes a triple bypass bus. The proposed router is highly fault-tolerant compared with the existing state-of-the-art fault-tolerant routers. The reliability of the proposed router is 7.98 times higher than that of the unprotected baseline router in terms of the mean-time-to-failure metric. The silicon protection factor metric is used to calculate the protection ability of the proposed router. Consequently, it is confirmed that the proposed router has a greater protection ability than the conventional fault-tolerant routers

NoCGuard: A Reliable Network-on-Chip Router Architecture

Aggressive scaling in deep nanometer technology enables chip multiprocessor design facilitated by the communication-centric architecture provided by Network-on-Chip (NoC). At the same time, it brings considerable challenges in reliability because a fault in the network architecture severely impacts the performance of a system. To deal with these reliability challenges, this research proposed NoCGuard, a reconfigurable architecture designed to tolerate multiple permanent faults in each pipeline stage of the generic router. NoCGuard router architecture uses four highly reliable and low-cost fault-tolerant strategies. We exploited resource borrowing and double routing strategy for the routing computation stage, default winner strategy for the virtual channel allocation stage, runtime arbiter selection and default winner strategy for the switch allocation stage and multiple secondary bypass paths strategy for the crossbar stage. Unlike existing reliable router architectures, our architecture features less redundancy, more fault tolerance, and high reliability. Reliability comparison using Mean Time to Failure (MTTF) metric shows 5.53-time improvement in a lifetime and using Silicon Protection Factor (SPF), 22-time improvement, which is better than state-of-the-art reliable router architectures. Synthesis results using 15 nm and 45 nm technology library show that additional circuitry incurs an area overhead of 28.7% and 28% respectively. Latency analysis using synthetic, PARSEC and SPLASH-2 traffic shows minor increase in performance by 3.41%, 12% and 15% respectively while providing high reliability

Improving Mispronunciation Detection of Arabic Words for Non-Native Learners Using Deep Convolutional Neural Network Features

Computer-Aided Language Learning (CALL) is growing nowadays because learning new languages is essential for communication with people of different linguistic backgrounds. Mispronunciation detection is an integral part of CALL, which is used for automatic pointing of errors for the non-native speaker. In this paper, we investigated the mispronunciation detection of Arabic words using deep Convolution Neural Network (CNN). For automated pronunciation error detection, we proposed CNN features-based model and extracted features from different layers of Alex Net (layers 6, 7, and 8) to train three machine learning classifiers; K-nearest neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). We also used a transfer learning-based model in which feature extraction and classification are performed automatically. To evaluate the performance of the proposed method, a comprehensive evaluation is provided on these methods with a traditional machine learning-based method using Mel Frequency Cepstral Coefficients (MFCC) features. We used the same three classifiers KNN, SVM, and RF in the baseline method for mispronunciation detection. Experimental results show that with handcrafted features, transfer learning-based method and classification based on deep features extracted from Alex Net achieved an average accuracy of 73.67, 85 and 93.20 on Arabic words, respectively. Moreover, these results reveal that the proposed method with feature selection achieved the best average accuracy of 93.20% than all other methods

Modelling Potential Distribution of Snow Leopards in Pamir, Northern Pakistan: Implications for Human–Snow Leopard Conflicts

The snow leopard (Panthera uncia) is a cryptic and rare big cat inhabiting Asia’s remote and harsh elevated areas. Its population has decreased across the globe for various reasons, including human–snow leopard conflicts (HSCs). Understanding the snow leopard’s distribution range and habitat interactions with human/livestock is essential for understanding the ecological context in which HSCs occur and thus gives insights into how to mitigate HSCs. In this study, a MaxEnt model predicted the snow leopard’s potential distribution and analyzed the land use/cover to determine the habitat interactions of snow leopards with human/livestock in Karakoram–Pamir, northern Pakistan. The results indicated an excellent model performance for predicting the species’ potential distribution. The variables with higher contributions to the model were the mean diurnal temperature range (51.7%), annual temperature range (18.5%), aspect (14.2%), and land cover (6.9%). The model predicted approximately 10% of the study area as a highly suitable habitat for snow leopards. Appropriate areas included those at an altitude ranging from 2721 to 4825 m, with a mean elevation of 3796.9 ± 432 m, overlapping between suitable snow leopard habitats and human presence. The human encroachment (human settlements and agriculture) in suitable snow leopard habitat increased by 115% between 2008 and 2018. Increasing encroachment and a clear overlap between snow leopard suitable habitat and human activities, signs of growing competition between wildlife and human/livestock for limited rangeland resources, may have contributed to increasing HSCs. A sound land use plan is needed to minimize overlaps between suitable snow leopard habitat and human presence to mitigate HSCs in the long run

Incorporating Noise Robustness in Speech Command Recognition by Noise Augmentation of Training Data

The advent of new devices, technology, machine learning techniques, and the availability of free large speech corpora results in rapid and accurate speech recognition. In the last two decades, extensive research has been initiated by researchers and different organizations to experiment with new techniques and their applications in speech processing systems. There are several speech command based applications in the area of robotics, IoT, ubiquitous computing, and different human-computer interfaces. Various researchers have worked on enhancing the efficiency of speech command based systems and used the speech command dataset. However, none of them catered to noise in the same. Noise is one of the major challenges in any speech recognition system, as real-time noise is a very versatile and unavoidable factor that affects the performance of speech recognition systems, particularly those that have not learned the noise efficiently. We thoroughly analyse the latest trends in speech recognition and evaluate the speech command dataset on different machine learning based and deep learning based techniques. A novel technique is proposed for noise robustness by augmenting noise in training data. Our proposed technique is tested on clean and noisy data along with locally generated data and achieves much better results than existing state-of-the-art techniques, thus setting a new benchmark