Faculty Publications
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Item Design and construction of BCH codes for enhancing data integrity in multi level flash memories(Inderscience Publishers, 2012) Rajesh Shetty, K.; Ramakrishna, K.; Prashantha Kumar, H.; Sripati, U.Flash memories have found extensive application for use in storage devices. The storage capacity and reliability of these devices have increased enormously over the years. With increase in density of data storage, the raw bit error rate (RBER), associated with the storage device increases. Error control coding (ECC) can be used to reduce the RBER to acceptable values so that these devices can be employed to store information in applications where data corruption is unacceptable. In this paper, we describe the synthesis of BCH codes for flash memories based on multi level cell (MLC) concept. This is in continuation of our work on synthesis of BCH codes for improving the performance of flash memories based on single level cells (SLC). The improvement in device integrity resulting from the use of these codes has been quantified in this paper along with computation of parameters which allows modelling of flash memory as an equivalent channel. While synthesising codes, we have adhered to the limitations imposed by the memory architecture. Use of these codes in storage devices will result in considerable enhancement of device reliability and consequently open up many new applications for this class of storage devices. © 2012 Inderscience Enterprises Ltd.Item Enhancing the error-correcting capability of imai-kamiyanagi codes for data storage systems by adopting iterative decoding using a parity check tree(2012) Kumar, H.; Sripati, U.; Rajesh Shetty, K.; Shankarananda, B.A novel low-complexity, soft decision technique which allows the decoding of distance-5 double error-correcting Imai-Kamiyanagi codes by using a parity check tree associated with the Tanner graph is proposed. These codes have been applied to memory subsystems and digital storage devices in order to achieve efficient and reliable data processing and storage. For the AWGN channel, gains in excess of 1.5 dB at reasonable bit error rates with respect to conventional hard decision decoding are demonstrated for the (46, 32), (81, 64), and (148, 128) shortened Imai-Kamiyanagi codes. Copyright © 2012 by the IETE.Item Accurate estimation of decay coefficients for dynamic range compressors in hearing aids and a hardware level comparison of different architectures(Elsevier B.V., 2020) Deepu, S.P.; Ramesh Kini, M.R.; Sumam David, S.S.Dynamic Range Compression (DRC) algorithm helps to protect the residual hearing ability of hearing aid users by compressing the signal levels which go above a particular threshold. This paper addresses two different aspects of DRC for hearing aid applications. In the first part, methods to estimate the decay coefficients corresponding to the required time constants for a feed-forward DRC architecture accurately, to meet the hearing aid specifications are proposed. The effect of compression on the attack and release time parameters are compensated with the new formula. The hardware implementation of four different DRC architectures is explained in the second part of the paper. The estimated decay coefficients for a test signal were used for the corresponding hardware implementations and verified the validity of proposed algorithmic modifications. The architectures were implemented using UMC 65 nm standard cell libraries and the power and error results were compared. The proposed methods to estimate the decay coefficients for both attack and release phases show close to 0 dB error from expected output values, while conventional methods are not meeting the specifications. Hardware implementation shows that there is not much improvement in power performance, between a lower resolution Look-Up Table (LUT) based logarithm implementation and a higher resolution one. From the results, we propose using the absolute level detector based DRC with higher resolution logarithm without a gain smoothing stage at the output for lowest power consumption and better approximation error performance. © 2020 Elsevier B.V.Item Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images(Elsevier Ltd, 2021) Aatresh, A.A.; Yatgiri, R.P.; Chanchal, A.K.; Kumar, A.; Ravi, A.; Das, D.; Raghavendra, B.S.; Lal, S.; Kini, J.Image segmentation remains to be one of the most vital tasks in the area of computer vision and more so in the case of medical image processing. Image segmentation quality is the main metric that is often considered with memory and computation efficiency overlooked, limiting the use of power hungry models for practical use. In this paper, we propose a novel framework (Kidney-SegNet) that combines the effectiveness of an attention based encoder-decoder architecture with atrous spatial pyramid pooling with highly efficient dimension-wise convolutions. The segmentation results of the proposed Kidney-SegNet architecture have been shown to outperform existing state-of-the-art deep learning methods by evaluating them on two publicly available kidney and TNBC breast H&E stained histopathology image datasets. Further, our simulation experiments also reveal that the computational complexity and memory requirement of our proposed architecture is very efficient compared to existing deep learning state-of-the-art methods for the task of nuclei segmentation of H&E stained histopathology images. The source code of our implementation will be available at https://github.com/Aaatresh/Kidney-SegNet. © 2021 Elsevier LtdItem An effective feature extraction with deep neural network architecture for protein-secondary-structure prediction(Springer, 2021) Jayasimha, A.; Mudambi, R.; Pavan, P.; Lokaksha, B.M.; Bankapur, S.; Patil, N.With the increased importance of proteins in day-to-day life, it is imperative to know the protein functions. Deciphering protein structure elucidates protein functions. Experimental approaches for protein-structure analysis are expensive and time-consuming, and require high dexterity. Thus, finding a viable computational approach is vital. Due to the high complexity of predicting protein structure (tertiary structure) directly, research in this field aims at the protein-secondary-structure prediction which is directly related to its tertiary structure. This research aims at exploring a plethora of features, namely position-specific scoring matrices, hidden Markov model alignment matrices, and physicochemical properties, that carry rich information required to predict the secondary structure. Furthermore, it aims at exploring a suitable combination of the features which could capture diverse information about the protein secondary structure. Finally, a cascaded convolutional neural network and bidirectional long short-term memory architecture is fit on the models, and two evaluation metrics, namely, Q8 score and segment overlap score, are benchmarked on various datasets. Our proposed model trained on data of CB6133 dataset and tested on CB513 dataset beats the benchmark models by a minimum of 2.9%. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.Item An efficient parallel-pipelined intra prediction architecture to support DCT/DST engine of HEVC encoder(Springer Science and Business Media Deutschland GmbH, 2022) Poola, L.; Aparna., P.The complexity of intra prediction in high-efficiency video coding (HEVC) is increased due to the addition of five variable sized prediction units (PUs) and 35 directional predictions. In this work, we propose an efficient parallel-pipelined architecture that can process 8 samples in parallel for every clock cycle. The functional units needed to predict the PU samples work in a pipelined fashion. With this balanced combination of parallel-pipelined structure, we are able to achieve higher throughput with limited hardware resources than existing literature works. The samples are processed row-wise, so that they can be directly transform coded, thus eliminating the need for an intermediate memory buffer of 8 K between the two modules. A compact reconfigurable reference buffer of size 0.8 KB is incorporated to reduce the read-write latency associated with reference samples’ fetching. A dedicated module for arithmetic operations is used in the intra engine that ensures the reuse of multipliers to increase the hardware efficiency. The architecture so designed supports all the PU sizes and directional modes. The proposed design is tested and implemented on a field-programmable gate array (FPGA) platform operating at 150 MHz frequency to achieve 8 samples throughput with a hardware cost of 16.2 K Look-Up Tables (LUTs) and 5.7 K registers to support HD 4 K real-time video encoding applications. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item On the Design of SSRS and RS Codes for Enhancing the Integrity of Information Storage in NAND Flash Memories(Institute of Electrical and Electronics Engineers Inc., 2023) Achala, G.; Shripathi Acharya, U.S.; Srihari, P.The revolution in the field of information processing systems has created a huge demand for reliable and enhanced data storage capabilities. This demand is being met by advances in channel coding algorithms along with upward scaling of the capacities of hardware devices. NAND Flash memory is a type of non-volatile memory. Scaling of the size of flash memories from Single Level Cell (SLC) devices to Multilevel cell (MLC) devices has increased the storage capacity. However, these multi-bit per cell architectures are characterized by significantly higher Raw Bit Error Rate (RBER) values when compared with SLC architectures. The requirement of low Undetected Bit Error Rate (UBER) values has motivated us to synthesize powerful channel codes for enhancing the integrity of information Storage in multi-level NAND Flash Memory devices. This paper describes the synthesis of novel Subfield Subcodes of Reed Solomon Codes (SSRS) and Reed-Solomon (RS) codes which are matched to multi-bit per cell architectures. UBER values have been calculated for each of the synthesized codes described in this paper. This allows the determination of the performance and the improvement in data storage integrity brought by using these codes. We have shown that the synthesized SSRS and RS codes can provide very low UBER even when the corresponding RBER values are appreciable. As RS codes permit the detection and correction of a greater number of errors for a given code length, their performance is superior to that of SSRS codes. This improved performance is obtained at the cost of greater complexity of encoding and decoding processes. © 2013 IEEE.Item Solar Irradiation Prediction Hybrid Framework Using Regularized Convolutional BiLSTM-Based Autoencoder Approach(Institute of Electrical and Electronics Engineers Inc., 2023) Chiranjeevi, M.; Karlamangal, S.; Moger, T.; Jena, D.Solar irradiance prediction is an essential subject in renewable energy generation. Prediction enhances the planning and management of solar installations and provides several economic benefits to energy companies. Solar irradiation, being highly volatile and unpredictable makes the forecasting task complex and difficult. To address the shortcomings of the traditional approaches, this research developed a hybrid resilient architecture for an enhanced solar irradiation forecast by employing a long short-term memory (LSTM) autoencoder, convolutional neural network (CNN), and the Bi-directional Long Short Term Memory (BiLSTM) model with grid search optimization. The suggested hybrid technique is comprised of two parts: feature encoding and dimensionality reduction using an LSTM autoencoder, followed by a regularized convolutional BiLSTM. The encoder is tasked with extracting the key features in order to deduce the input into a compact latent representation. The decoder network then predicts solar irradiance by analyzing the encoded representation's attributes. The experiments are conducted on three publicly available data sets collected from Desert Knowledge Australia Solar Centre (DKASC), National Solar Radiation Database (NSRDB), and Hawaii Space Exploration Analog and Simulation (HI-SEAS) Habitat. The analysis of univariate and multivariate-multi step ahead forecasting performed independently and it is compared with the conventional approaches. Several benchmark forecasting models and three performance metrics are utilized to validate the hybrid approach's prediction performance. The results show that the proposed architecture outperforms benchmark models in accuracy. © 2013 IEEE.Item A Detailed Study of SOT-MRAM as an Alternative to DRAM Primary Memory in Multi-Core Environment(Institute of Electrical and Electronics Engineers Inc., 2024) Kallinatha, H.D.; Rai, S.; Talawar, B.As the current primary memory technology is reaching its limits, it is essential to explore alternative memory technologies to accommodate modern applications and use cases. However, using new memory technology poses the challenge of deriving accurately estimated parameters for integrating new memory technology and doing reliable simulations. This study proposes a new approach incorporating Spin-Orbit-Torque-Magnetic-RAM (SOT-MRAM) into hybrid and full main memory architectures within a multi-core system, encompassing various memory configurations and capacities. The study addresses the challenge of evaluating SOT-MRAM-based memory systems when specific SOT-MRAM memory parameters are not publicly available. The research methodology includes micro-architectural (circuit-level) design space exploration and comprehensive full system simulations, which evaluate benchmark programs representing diverse application domains. The evaluation includes three memory structures with varying memory organizations and capacities. The results show that SOT-MRAM is a robust replacement for DRAM or hybrid memory, offering compelling advantages such as a remarkable 74.05% reduction in power consumption, a noteworthy 40.10% increase in bandwidth utilization, and a significant 72.85% reduction in Energy-Delay Product (EDP). The maximum latency penalties are also minimal, with a 3.71% increase for hybrid structures and a mere 0.07% for standalone SOT-MRAM memory structures. © 2013 IEEE.Item A unified vehicle trajectory prediction model using multi-level context-aware graph attention mechanism(Springer, 2024) Sundari, K.; Senthil Thilak, A.S.Predicting the mobility patterns of vehicles together with their interactions among surrounding traffic objects is a critical task in autonomous driving systems. Existing graph neural network-based trajectory prediction models primarily capture the structural connectivity of network nodes (road objects) and assume equal priority to all neighbors of a node. However, in real-time traffic networks, the behavior of each vehicle is significantly influenced by its neighboring road objects and this influence is not uniform. This necessitates a neighbor interaction-aware trajectory prediction model that assumes non-uniform priority among neighboring nodes. In this article, we have designed a novel unified trajectory prediction model which is suitable for both highway and urban traffic conditions. The proposed approach seamlessly integrates multi-level context modeling using graph attention mechanisms, capturing and leveraging interactions and dependencies among objects at varied levels of proximity within a graph. Additionally, it employs an encoder–decoder long short-term memory architecture for long-term trajectory prediction, ensuring adaptability to different driving scenarios. The advanced graph attention mechanisms play a crucial role in modeling spatial dependencies between vehicles, allowing the proposed model to dynamically adapt to evolving interactions over time. The experimentations done on real-world trajectory datasets, namely, Next Generation Simulation US-101 highway dataset and diverse urban datasets such as ApolloScape and Argoverse demonstrate remarkable performance of MC-GATP in long-term trajectory prediction. The model showcases superior prediction accuracy, scalability, and computational efficiency for both highway and urban environments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.