Searched for: subject%3A%22Hardware%255C+Acceleration%22
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LIN, Jinhuang (author)
Recent trends in machine learning (ML) have placed a strong emphasis on power- and resource-efficient neural networks, as well as the development of neural networks on edge devices. Spiking neural net-works (SNNs), due to their event-based nature, are one of the most promising types of neural networks for low-power applications. To accelerate...
master thesis 2023
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Knops, Per (author)
X-ray imaging systems play an important role in the diagnostic process of various medical conditions. Generating an accurate artificial X-ray image has multiple advantages. It allows for flexible configurations during generation. The resulting images can reduce testing time and cost, help the training of surgeons, and increase the amount of data...
master thesis 2023
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Shen, Zhaofeng (author)
Nowadays, to reduce the dependence of devices on cloud servers, machine learning workloads are required to process data on the edge. Furthermore, to improve adaptability to uncontrolled environments, there is a growing need for on-chip learning. Limitations in power and area for edge devices have increased interest in low-cost neural network...
master thesis 2023
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Yang, Yufeng (author)
Event-based cameras promise new opportunities for smart vision systems deployed at the edge. Contrary to their conventional frame-based counterparts, event-based cameras generate temporal light intensity changes as events on a per-pixel basis, enabling ultra-low latency with microsecond-scale temporal resolution, low power consumption at...
master thesis 2023
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Shahroodi, Taha (author), Miao, Michael (author), Zahedi, M.Z. (author), Wong, J.S.S.M. (author), Hamdioui, S. (author)
The high execution time of DNA sequence alignment negatively affects many genomic studies that rely on sequence alignment results. Pre-alignment filtering was introduced as a step before alignment to reduce the execution time of short-read sequence alignment greatly. With its success, i.e., achieving high accuracy and thus removing...
conference paper 2023
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Chen, Qinyu (author), Chang, Yaoxing (author), Kim, Kwantae (author), Gao, C. (author), Liu, Shih Chii (author)
Keyword spotting (KWS) is an important task on edge low-power audio devices. A typical edge KWS system consists of a front-end feature extractor which outputs mel-scale frequency cepstral coefficients (MFCC) features followed by a back-end neural network classifier. KWS edge designs aim for the best power-performance-area metrics. This work...
conference paper 2023
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Jiang, Longxing (author), Aledo Ortega, D. (author), van Leuken, T.G.R.M. (author)
Logarithmic quantization for Convolutional Neural Networks (CNN): a) fits well typical weights and activation distributions, and b) allows the replacement of the multiplication operation by a shift operation that can be implemented with fewer hardware resources. We propose a new quantization method named Jumping Log Quantization (JLQ). The key...
conference paper 2023
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Jiang, Longxing (author)
Convolutional Neural Networks (CNN) have become a popular solution for computer vision problems. However, due to the high data volumes and intensive computation involved in CNNs, deploying CNNs on low-power hardware systems is still challenging.<br/>The power consumption of CNNs can be prohibitive in the most common implementation platforms:...
master thesis 2022
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Mrahorović, Mirza (author)
Deep Neural Network (DNNs) have increased significantly in size over the past decade. Partly due to this, the accuracy of DNNs in image classification and speech recognition tasks has increased as well. This enables a great potential for such models to be applied in real-world applications. However, due to their size, the compute and power...
master thesis 2021
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Yönsel, Yüksel (author)
There has been an increasing interest in moving computation closer to storage in recent years due to significant improvements in memory technology. FPGAs were proven to be an exciting candidate for accelerating database workloads since they provide an energy-efficient, reconfigurable and high-performance computation platform. Therefore, FPGAs...
master thesis 2021
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Hadnagy, A. (author)
Recent trends in large-scale computing demonstrate continuous growth in the need for raw processing performance. At the same time, the slowdown of vertical scaling pushes the industry towards more energy-efficient heterogeneous architectures. With the appearance of FPGAs in the cloud and data centers, a new architecture is offered for offloading...
master thesis 2020
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Noordam, Leon (author)
Modular exponentiation is the basis needed to perform RSA encryption and decryption. Execution of 4096-bit modular exponentiation using an embedded system requires many arithmetic operations. This work aims to improve the performance of modular exponentiation for an existing FPGA platform containing a soft core RISC-V processor. The solution is...
master thesis 2019
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De Wit, R. (author)
The design of a protocol parser in hardware based on language theory to improve time-to-market, reduce development cost and increase performance compared to protocol parsing in software.
master thesis 2015
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Pham-Quoc Cuong, P. (author)
Heterogeneous multicore systems are becoming increasingly important as the need for computation power grows, especially when we are entering into the big data era. As one of the main trends in heterogeneous multicore, hardware accelerator systems provide application specific hardware circuits and are thus more energy efficient and have higher...
doctoral thesis 2015
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Zaykov, P.G. (author)
In this dissertation, we address the problem of performance efficient multithreading execution on heterogeneous multicore embedded systems. By heterogeneous multicore embedded systems we refer to those, which have real-time requirements and consist of processor tiles with General Purpose Processor (GPP), local memory, and one or more...
doctoral thesis 2014
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Viswanathan, V. (author)
Dynamically reconfigurable architectures have demonstrated superior performance in comparison to the general-purpose processors. This thesis describes a generic approach for Dynamic Partial Reconfiguration (DPR) of a reconfigurable platform, connected to a general purpose system through a high-speed interconnect. Thus, the system can dynamically...
master thesis 2011
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Hasan, L. (author)
Biological sequence alignment is an important and challenging task in bioinformatics. Alignment may be defined as an arrangement of two or more DNA or protein sequences to highlight the regions of their similarity. Sequence alignment is used to infer the evolutionary relationship between a set of protein or DNA sequences. An accurate alignment...
doctoral thesis 2011
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Van Wijnen, P.A. (author)
This thesis presents a feasibility analysis for hardware acceleration of the pattern recognition algorithms used by the Media Knowledge Engineering department at the Delft University of Technology. The feasibility analysis is conducted on a number of different algorithm classes. The Parzen Window algorithm appeared to be the most suitable option...
master thesis 2009
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Kuzmanov, G.K. (author)
In this dissertation, we address high performance media processing based on a tightly coupled co-processor architectural paradigm. More specifically, we introduce a reconfigurable media augmentation of a general purpose processor and implement it into a fully operational processor prototype. The proposed media Molen prototype is implemented on...
doctoral thesis 2004
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Glossner III, C.J. (author)
In this dissertation, we describe the DELFT-JAVA engine - a 32-bit RISC-based architecture that provides high performance JAVA program execution. More specifically we describe a microarchitecture that accelerates JAVA execution and provide details of the DELFT-JAVA architecture for executing JAVA Virtual Machine bytecode. The basic architecture...
doctoral thesis 2001
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