3D Network-on-Chip (NoC) technology has emerged as a compelling solution in modern System-on-Chip (SoC) designs. This NoC technology effectively addresses the escalating need for high-performance and energy-efficient on-chip communication in various applications, including High-Performance Computing (HPC), Graphics Processing Units (GPUs), and Multi-Processor SoCs (MPSoCs). However, the efficient mapping of applications onto 3D NoCs remains a complex challenge, necessitating the development of improved algorithms to address the issue. In this context, we present a novel neural mapping model with a reinforcement learning (RL) approach (NeurMap3D) to design application-specific 3D NoC-based IC. Additionally, we propose the NCTPAM (neural congestion-aware Through-Silicon Vias (TSVs) placement and application mapping) approach, which not only addresses application mapping but also incorporates TSVs placement and load balance across the TSVs for the specific application. In order to reduce the CPU execution time of NCTPAM algorithm, we propose incorporating a partial model parameter (θ) update mechanism. Experimental results indicate improved performance in terms of minimizing communication cost, load balancing across TSVs and energy consumption, highlighting the potential of our approach to enhance the efficiency of these synthesized network architectures.

Finding an optimal application mapping solution in a manycore processor is an NP-hard problem. Heuristic search techniques have the advantage of finding near-optimal solutions faster than other methods when mapping large-scale applications. However, the majority of the heuristic-based application mapping methods easily fall into local minima. Machine learning (ML) methods can learn heuristics from training data on their own, require minimal assistance from humans, and produce better mapping solutions. Recently, a reinforcement learning-based framework (RLF) has been proposed to generate the initial population for metaheuristics, designed using genetic algorithm (GA) and particle swarm optimization (PSO). The RLF framework does not incorporate reward information while generating mapping solutions. However, the model performance can be improved further by refining the network parameters using the reward information during predictions. To overcome this challenge, we propose an active search framework (ASF). For the first time, we propose a new intellectual property (IP)-core numbering scheme, which will assist ASF in learning the mapping rules more effectively. We demonstrate that REINFORCE with multiple samples (predictions) per data point improves model accuracy and reduces variance by constructing a baseline using these samples. With these, we propose two RL models: active search (ATSR) and active search with pretraining (ATSRP). According to experimental results, both ATSRP and ATSR models produce better mapping solutions compared to RLF and other state-of-the-art methods. The results suggest that the ATSRP model is better suited for performing application mapping onto a 2-D mesh-based manycore processor. Finally, we extend this framework to other performance metrics and 3-D mesh-based manycore processors.

Wireless sensor networks (WSNs) find application in various fields like environmental monitoring, health-care, land security, and many more. To ease our day-to-day activity, WSNs have become an integral tool for complex data gathering tasks. Monitoring a phenomenon by a WSN depends on the collective data provided by the sensor nodes. To ensure reliable operation of WSNs, it is important to quantify the performance of such networks in terms of network reliability measures. This article studies the reliability of WSNs with multistate nodes and proposes an approach to evaluate the flow-oriented network reliability of WSNs consisting of multistate sensor nodes. The proposed method takes into account the dynamic state of the network due to multistate sensor nodes. The proposed approach includes enumeration of shortest minimal paths from application-specific flow satisfying sensor nodes (source nodes) to the sink node. It then proposes a modified sum-of-disjoint products approach to evaluate WSN reliability in the presence of multistate nodes from the enumerated shortest minimal paths. Simulations are performed on WSNs of various sizes to show the applicability of the proposed approach on arbitrary WSNs.

Day by day more and more devices are getting connected to the Internet and with the advent of the Internet of Things, this rate has had an exponential growth. The lack of security in devices connected to the IoT is making them hot targets for cyber-criminals and strength of botnet attacks have increased drastically. Botnets are the technological backbones of multitudinous attacks including Distributed Denial of Service (DDoS), SPAM, identity theft and organizational spying. The 2016 Dyn cyber attack involved multiple DDoS attacks with an estimated throughput of 1.2 terabits per second; the attack is the largest DDoS attack on record. In this paper, we compare three different techniques for botnet detection with each having its unique use cases. The results of the detection methods were verified using ISCX Intrusion Detection Dataset and the CTU-13 Dataset.

Multi‐pass encoding is preferred over single‐pass encoding for applications such as television broadcasting and video on demand to obtain a consistent visual quality media. This study presents a novel pyramid coding based two‐pass rate control scheme for variable bit rate video streaming. The proposed algorithm uses the statistics collected in the first pass to derive an optimal quantisation parameter for encoding the frames in the second pass. Simple linear estimation models are employed along with Gaussian filtering to update the frame quantisation parameter values in an adaptive manner, resulting in a smoother variation among these values and a better visual quality. The experimental results demonstrate that the proposed rate control scheme exhibits a better performance in terms of the peak signal‐to‐noise ratio and consistent perceptual visual quality in comparison with the existing rate control scheme in the joint model reference software while achieving the target bit rate.

In this paper, a novel two-pass rate control scheme is proposed to achieve a consistent visual quality media for variable bit rate (VBR) video streaming. The rate-distortion (RD) characteristics of each frame is used to establish a frame complexity model, which is later used along with statistics collected in the first-pass to derive an optimal quantization parameter for encoding the frame in the second-pass. The experimental results demonstrate that the proposed rate control scheme significantly outperforms the existing rate control mechanism in the Joint Model (JM) reference software in terms of the Peak Signal to Noise Ratio (PSNR) and consistent perceptual visual quality while achieving the target bit rate. Further, the proposed scheme is validated through implementation on a miniature test-bed.

Investigates the proposed implementation of neural networks on massively parallel hierarchical computer systems with hypernet topology. The proposed mapping scheme takes advantage of the inherent structure of hypernets to process multiple copies of the neural network in the different subnets, each executing a portion of the training set. Finally, the weight changes in all the subnets are accumulated to adjust the synaptic weights in all the copies. An expression is derived to estimate the time for all-to-all broadcasting, the principal mode of communication in implementing neural networks on parallel computers. This is later used to estimate the time required to execute various execution phases in the neural network algorithm, and thus to estimate the speedup performance of the hypernet in implementing neural networks.