Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Analysis of ring topology for NoC architecture(Institute of Electrical and Electronics Engineers Inc., 2016) Kamath, A.; Saxena, G.; Talawar, B.In recent years, Network on Chips (NoCs) have provided an efficient solution for interconnecting various heterogeneous intellectual properties (IPs) on a System on Chip (SoC) in an efficient, flexible and scalable manner. Virtual channels in the buffers associated with the core helps in introducing the parallelism between the packets as well as in improving the performance of the network. However, allocating a uniform size of the buffer to these channels is not always suitable. The network efficiency can be improved by allocating the buffer variably based on the traffic patterns and the node requirements. In this paper, we use ring topology as an underlying architecture for the NoC. The percentage of packet drops has been used as a parameter for comparing the performance of different architectures. Through the results of the simulations carried out in SystemC, we illustrate the impact of including virtual channels and variable buffers on the network performance. As per our results, we observed that varied buffer allocation led to a better performance and fairness in the network as compared to that of the uniform allocation. © 2015 IEEE.Item Certified mergeable replicated data types(Association for Computing Machinery, 2022) Soundarapandian, V.; Kamath, A.; Nagar, K.; Sivaramakrishnan, K.C.Replicated data types (RDTs) are data structures that permit concurrent modification of multiple, potentially geo-distributed, replicas without coordination between them. RDTs are designed in such a way that conflicting operations are eventually deterministically reconciled ensuring convergence. Constructing correct RDTs remains a difficult endeavour due to the complexity of reasoning about independently evolving states of the replicas. With the focus on the correctness of RDTs (and rightly so), existing approaches to RDTs are less efficient compared to their sequential counterparts in terms of the time and space complexity of local operations. This is unfortunate since RDTs are often used in a local-first setting where the local operations far outweigh remote communication. This paper presents PEEPUL, a pragmatic approach to building and verifying efficient RDTs. To make reasoning about correctness easier, we cast RDTs in the mould of the distributed version control system, and equip it with a three-way merge function for reconciling conflicting versions. Further, we go beyond just verifying convergence, and provide a methodology to verify arbitrarily complex specifications. We develop a replication-aware simulation relation to relate RDT specifications to their efficient purely functional implementations. We implement PEEPUL as an F∗library that discharges proof obligations to an SMT solver. The verified efficient RDTs are extracted as OCaml code and used in Irmin, a distributed database built on the principles of Git. © 2022 Owner/Author.Item Anomaly Detection in Electric Powertrain System Software Behaviour(Institute of Electrical and Electronics Engineers Inc., 2023) Vyas, A.; Ghorpade, V.; Kamble, S.; Johnson, P.S.; Kamath, A.; Rawat, K.A software-in-loop (SIL) testing is a method of early testing of control software of a car in virtual environment. A system level testing is carried out on regular basis and it is important to see, if system is behaving as expected or unexpected. For unexpected behaviors, which test engineers not easily notice, modern techniques such as machine learning can give an advantage. This paper presents an application of machine learning algorithms that helps in identifying the abnormal patterns in time series data generated from electric powertrain system testing done in SIL environment for a Mercedes Benz Electric Car. Output of the SIL testing, results in time series data that is a collection of observations that are ordered chronologically and can be used to analyze trends, patterns, and changes over time. Anomaly detection in time series data is a process in machine learning that identifies data points, events, and observations that deviate from a dataset's normal behavior. By monitoring the expected and unexpected behavior of the electric powertrain system, anomaly detection can be a valuable tool for identifying potential issues. This study aims at coming up with an efficient process for anomaly detection in SIL. In order to get this process, various anomaly detection techniques are compared to detect a defined anomaly in time series data. Data pre-processing methods are also discussed before training the model. At the end, we conclude a best-fit method for identified anomaly. With finally identified method, a model was trained and used further in application. © 2023 IEEE.Item DeepVNE: Deep Reinforcement and Graph Convolution Fusion for Virtual Network Embedding(Institute of Electrical and Electronics Engineers Inc., 2024) Keerthan Kumar, T.G.; Kb, A.; Siddheshwar, A.; Marali, A.; Kamath, A.; Koolagudi, S.G.; Addya, S.K.Network virtualization (NV) plays a crucial role in modern network management. One of the fundamental challenges in NV is allocating physical network (PN) resources to the demands of the virtual network requests (VNRs). This process is known as a virtual network embedding (VNE) and is NP-hard. Most of the existing approaches for VNE are based on heuristic, meta-heuristic, and exact strategies with limited flexibility and the risk of being stuck in local optimal solutions. In this concern, we provide a deep reinforcement learning (DRL) and graph convolution network (GCN) fusion for VNE (DeepVNE) for maximizing the revenue-to-cost ratio. The DeepVNE takes advantage of the power of actor-critic models within the DRL framework to detect network states and provide optimal solutions matched to current conditions. DeepVNE effectively captures the structural dependencies of both VNRs and PN resources by GCNs, allowing better decision-making during the embedding. Considering several features in the agents throughout the training phase improves resource utilization. The experiments show that DeepVNE outperforms the baselines by gaining a 51% acceptance ratio and 28% revenue-to-cost ratio. © 2024 IEEE.