Level shifters (LS) are fundamental components in modern integrated circuits, facilitating voltage translation between different domains in mixed-signal and low-power designs. this review paper presents a comprehensiv...
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ISBN:
(数字)9798350384369
ISBN:
(纸本)9798350384376
Level shifters (LS) are fundamental components in modern integrated circuits, facilitating voltage translation between different domains in mixed-signal and low-power designs. this review paper presents a comprehensive analysis of Very Large-Scale Integration (VLSI) implementations of level shifters, focusing on design techniques, architectures, and performance metrics. the significance of level shifters in both analog and digital systems is discussed, emphasizing their role in ensuring compatibility and efficient signal processing. Furthermore, the continuous development of LS designs is explored in addressing challenges associated with scaling down technology nodes, enhancing energy efficiency, reducing leakage currents, and improving speed and reliability. the review also highlights the impact of LS circuits on overall system performance and efficiency. In conclusion, this paper provides valuable insights into the design and application of level shifters in modern electronic systems, paving the way for future advancements in the field.
Cluster Computing Systems (CCS) is a type of technology that not only causes computing power improvement but also utilizes energy to a lesser degree by taking advantage of parallel programming while processing and rea...
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ISBN:
(数字)9798331596651
ISBN:
(纸本)9798331596668
Cluster Computing Systems (CCS) is a type of technology that not only causes computing power improvement but also utilizes energy to a lesser degree by taking advantage of parallel programming while processing and reading massive amounts of data. We can have multiple Central processing Units (CPUs) and storage devices (disks) where the massive size of data can be processed. However, Cluster Computing System also comes with its own set of challenges such as if for a reason the node stops operating, nodes stops communicating with each other and the data transfer doesn’t happen due to poor network which can lead to bottleneck while processing massive amounts of data. To overcome these issues, a well reputed tech giant known as Google, came up with a solution known as MapReduce. MapReduce is a framework designed for Big Data which takes care of processing large amounts of data over various servers. In this paper, we outline how CCS works and the challenges it faces today in the age of massive data. the introduction to some well received measures of Big Data are presented by us in this paper. these solutions show us the way we can address the issues we face in CSS. the primary goal of this writing is to look into the issues that we might face and the most efficient ways to resolve it in CSS.
Convolutional neural networks (CNNs) are extensively employed for tasks such as image processing, employing a hierarchical feature extraction approach facilitated by convolutional layers. A recent trend explores quant...
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ISBN:
(数字)9798350373172
ISBN:
(纸本)9798350373189
Convolutional neural networks (CNNs) are extensively employed for tasks such as image processing, employing a hierarchical feature extraction approach facilitated by convolutional layers. A recent trend explores quantum counterparts called Quanvolutional layers, utilizing random quantum circuits akin to convolutional filter layers. Our work advances this by introducing structured entanglement in quanvolutional layers using Basic and Strongly Entangled Layers. Unlike random circuit approaches, these layers have defined structures and high entanglement, requiring minimal error correction. We evaluated their performance on MNIST dataset, comparing various techniques like traditional CNNs, Quanvolutional Neural Networks (QNNs) with Random quantum circuits, basic entangled layers, strongly entangled layers. Structured entanglement shows promise in improving feature extraction for QNNs, providing insights into their understanding. Our work explores the use of structures quantum layers in constructing quantum-classical hybrid architectures and evaluates their performance. Future research may explore scalability and applicability in complex datasets and integration with other quantum computing techniques for enhanced performance and versatility.
Search trees are one of the most important and widely used data structures, and parallelization is an effective method to improve their performance. However, many existing parallel search trees incur high synchronizat...
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ISBN:
(纸本)9781665410168
Search trees are one of the most important and widely used data structures, and parallelization is an effective method to improve their performance. However, many existing parallel search trees incur high synchronization costs and low memory I/O efficiency, which limits their performance. We propose PPBT, a batched parallel search tree which minimizes synchronization by partitioning the tree using novel algorithms and minimizing I/O cost using buffering. We give a new sequential algorithm for batch processing on search trees with optimal I/O efficiency for insert and delete operations, and also present a fast parallel algorithm for joining disjoint search trees. We show experimentally that PPBT is over 6x faster than the state-of-the-art parallel tree in [1] and over 40x faster than the concurrent search tree in [7], and achieves 21x speedup using 32 threads. PPBT's throughput on searches is lower due to reduced opportunities for buffering, but is still 1.3x that of [1]. In addition, PPBT has good response times for searches, for example completing 100K searches in under 1 ms in a tree with10M elements.
As the continuously increasing demand of high data rate and lower transmission, many modern communication systems employ the LDPC coding scheme with ultra-high throughput decoder to achieve these challenging requireme...
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ISBN:
(数字)9798350390643
ISBN:
(纸本)9798350390650
As the continuously increasing demand of high data rate and lower transmission, many modern communication systems employ the LDPC coding scheme with ultra-high throughput decoder to achieve these challenging requirements. However, due to the implementation complexity of decoding algorithm, designing an ultra-high throughput decoder of LDPC codes still suffers many difficulties. therefore, we propose a FPGA-based LDPC decoder with partial parallel decoding, which splits and virtually supplements the parity-check matrix to achieve higher throughput and lower latency. Our proposed FPGA-based decoder can achieve a throughput of 6.072Gbps in 10 iterations for the highest rate 5G-LDPC code.
Programming efficiently heterogeneous systems is a major challenge, due to the complexity of their architectures. Intel oneAPI, a new and powerful standards-based unified programming model, built on top of SYCL, addre...
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ISBN:
(纸本)9783030856656;9783030856649
Programming efficiently heterogeneous systems is a major challenge, due to the complexity of their architectures. Intel oneAPI, a new and powerful standards-based unified programming model, built on top of SYCL, addresses these issues. In this paper, oneAPI is provided with co-execution strategies to run the same kernel between different devices, enabling the exploitation of static and dynamic policies. On top of that, static and dynamic load-balancing algorithms are integrated and analyzed. this work evaluates the performance and energy efficiency for a well-known set of regular and irregular HPC benchmarks, using an integrated GPU and CPU. Experimental results show that co-execution is worthwhile when using dynamic algorithms, improving efficiency even more when using unified shared memory.
In recent years, the convergence of cognitive computing and natural language processing (NLP) has emerged as a critical field of study, promising significant breakthroughs in medical imaging. this research digs into t...
In recent years, the convergence of cognitive computing and natural language processing (NLP) has emerged as a critical field of study, promising significant breakthroughs in medical imaging. this research digs into the integration of cognitive computing approaches with NLP to increase the interpretation and comprehension of complicated medical narratives. We offer a unique framework that harnesses the cognitive capacities of computers to process, analyze, and interpret huge quantities of unstructured medical material. Our technique combines deep learning architectures and semantic analysis to extract therapeutically important information from radiology reports, patient histories, and other textual data sources. Preliminary findings suggest a considerable increase in the accuracy and efficiency of medical picture annotations, leading to more accurate diagnostic insights. Furthermore, the system exhibits an adeptness in understanding sophisticated medical jargons, acronyms, and context-dependent interpretations. this discovery not only emphasizes the promise of cognitive computing in changing medical imaging but also establishes a precedent for its use in other sectors needing complex language interpretation.
Partial shading faults on photovoltaic (PV) modules can lead to power reduction, hot spots, and life reduction. Although the shaded modules can be bypassed by the bypass diodes, the peak power produced is lower than t...
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ISBN:
(数字)9798350351330
ISBN:
(纸本)9798350351347
Partial shading faults on photovoltaic (PV) modules can lead to power reduction, hot spots, and life reduction. Although the shaded modules can be bypassed by the bypass diodes, the peak power produced is lower than the ideal values. In this paper, a differential power processing scheme with a shadow fault detection method is proposed for two parallel-connection PV strings. the method utilizes normalized error (DE) of the comparison of the I-V curve in normal operation and under partial shading conditions to define whether PV cells dissipated power. When the shadow fault is detected, the proposed voltage equalizer would operate to eliminate the unbalanced voltage and power and improve the peak output power. the voltage equalizer use the series resonant voltage multiplier (SRVM) to realize the aims.
In today’s industrial landscape, automation has become increasingly vital, particularly in the deployment of robots for tasks such as sorting machine components. the use of robotic systems enhances process accuracy a...
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ISBN:
(数字)9798331509972
ISBN:
(纸本)9798331509989
In today’s industrial landscape, automation has become increasingly vital, particularly in the deployment of robots for tasks such as sorting machine components. the use of robotic systems enhances process accuracy and speed, resulting in significant cost reductions and improved productivity compared to manual labor. this paper aims to design and develop an automated sorting system for various types of mechanical and electrical parts, utilizing image processing and machine vision algorithms with a Delta parallel robot equipped with a two-finger gripper. the target mechanical and electrical parts in this study are screws, nuts, metal washers, rubber washers, retaining rings, rectangular keys, wall plugs, resistors, potentiometers, capacitors, batteries, ICs, and LEDs. the YOLOv3 algorithm and Adaptive thresholding method are employed to detect and distinguish objects from the background, and size measurement is achieved withthe help of a custom marker with known dimensions. In this study, transfer learning based on pre-trained weights of YOLOv3 for the COCO dataset is applied. the proposed system attains a final mean Average Precision (mAP@0.5) value exceeding 0.95 for part detection using YOLOv3. Additionally, it demonstrates an overall pick-and-place success rate exceeding $\mathbf{9 0 \%}$.
High-performance electronics has fueled the rich emergence of medical imaging applications that led to the exponential growth in treatment and diagnostic solutions of various medical problems. High-throughput and Ener...
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ISBN:
(纸本)9781665440875
High-performance electronics has fueled the rich emergence of medical imaging applications that led to the exponential growth in treatment and diagnostic solutions of various medical problems. High-throughput and Energy-efficient systems are required to enable the development of complex medical imaging applications. this article presents an energy-efficient hardware-software (HW-SW) co-design of a scalable and reconfigurable image segmentation/classification streaming-based processing platform explored at various design abstraction levels. Optimized algorithms and architectural techniques achieve significant savings in energy consumption and operational time. the proposed platform has been implemented on Xilinx Spartan-6 FPGA board and co-simulated with Xilinx system generator, enabled real-time processing of CT scans for pulmonary nodule detection. Optimized pipelining and scheduling have minimized the memory requirements to few kB. parallel architecture has been employed achieving 10x higher energy-efficiency compared to serial counterpart and reduced execution period by 70x. Clinical validation shows that parallel architecture introduces 5-7% error in nodule characteristic determination in comparison to serial one.
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