Field-programmable gate array
About: Field-programmable gate array is a(n) research topic. Over the lifetime, 36074 publication(s) have been published within this topic receiving 354374 citation(s). The topic is also known as: FPGA & Field-Programmable Gate Array.
Papers published on a yearly basis
TL;DR: The hardware aspects of reconfigurable computing machines, from single chip architectures to multi-chip systems, including internal structures and external coupling are explored, and the software that targets these machines is focused on.
Abstract: Due to its potential to greatly accelerate a wide variety of applications, reconfigurable computing has become a subject of a great deal of research. Its key feature is the ability to perform computations in hardware to increase performance, while retaining much of the flexibility of a software solution. In this survey, we explore the hardware aspects of reconfigurable computing machines, from single chip architectures to multi-chip systems, including internal structures and external coupling. We also focus on the software that targets these machines, such as compilation tools that map high-level algorithms directly to the reconfigurable substrate. Finally, we consider the issues involved in run-time reconfigurable systems, which reuse the configurable hardware during program execution.
TL;DR: Experimental measurements of the differences between a 90- nm CMOS field programmable gate array (FPGA) and 90-nm CMOS standard-cell application-specific integrated circuits (ASICs) in terms of logic density, circuit speed, and power consumption for core logic are presented.
Abstract: This paper presents experimental measurements of the differences between a 90-nm CMOS field programmable gate array (FPGA) and 90-nm CMOS standard-cell application-specific integrated circuits (ASICs) in terms of logic density, circuit speed, and power consumption for core logic. We are motivated to make these measurements to enable system designers to make better informed choices between these two media and to give insight to FPGA makers on the deficiencies to attack and, thereby, improve FPGAs. We describe the methodology by which the measurements were obtained and show that, for circuits containing only look-up table-based logic and flip-flops, the ratio of silicon area required to implement them in FPGAs and ASICs is on average 35. Modern FPGAs also contain "hard" blocks such as multiplier/accumulators and block memories. We find that these blocks reduce this average area gap significantly to as little as 18 for our benchmarks, and we estimate that extensive use of these hard blocks could potentially lower the gap to below five. The ratio of critical-path delay, from FPGA to ASIC, is roughly three to four with less influence from block memory and hard multipliers. The dynamic power consumption ratio is approximately 14 times and, with hard blocks, this gap generally becomes smaller
••21 Feb 2016
TL;DR: This paper presents an in-depth analysis of state-of-the-art CNN models and shows that Convolutional layers are computational-centric and Fully-Connected layers are memory-centric, and proposes a CNN accelerator design on embedded FPGA for Image-Net large-scale image classification.
Abstract: In recent years, convolutional neural network (CNN) based methods have achieved great success in a large number of applications and have been among the most powerful and widely used techniques in computer vision. However, CNN-based methods are com-putational-intensive and resource-consuming, and thus are hard to be integrated into embedded systems such as smart phones, smart glasses, and robots. FPGA is one of the most promising platforms for accelerating CNN, but the limited bandwidth and on-chip memory size limit the performance of FPGA accelerator for CNN.In this paper, we go deeper with the embedded FPGA platform on accelerating CNNs and propose a CNN accelerator design on embedded FPGA for Image-Net large-scale image classification. We first present an in-depth analysis of state-of-the-art CNN models and show that Convolutional layers are computational-centric and Fully-Connected layers are memory-centric.Then the dynamic-precision data quantization method and a convolver design that is efficient for all layer types in CNN are proposed to improve the bandwidth and resource utilization. Results show that only 0.4% accuracy loss is introduced by our data quantization flow for the very deep VGG16 model when 8/4-bit quantization is used. A data arrangement method is proposed to further ensure a high utilization of the external memory bandwidth. Finally, a state-of-the-art CNN, VGG16-SVD, is implemented on an embedded FPGA platform as a case study. VGG16-SVD is the largest and most accurate network that has been implemented on FPGA end-to-end so far. The system on Xilinx Zynq ZC706 board achieves a frame rate at 4.45 fps with the top-5 accuracy of 86.66% using 16-bit quantization. The average performance of convolutional layers and the full CNN is 187.8 GOP/s and 137.0 GOP/s under 150MHz working frequency, which outperform previous approaches significantly.
TL;DR: This paper reviews the state of the art of field- programmable gate array (FPGA) design methodologies with a focus on industrial control system applications and presents three main design rules, algorithm refinement, modularity, and systematic search for the best compromise between the control performance and the architectural constraints.
Abstract: This paper reviews the state of the art of field- programmable gate array (FPGA) design methodologies with a focus on industrial control system applications. This paper starts with an overview of FPGA technology development, followed by a presentation of design methodologies, development tools and relevant CAD environments, including the use of portable hardware description languages and system level programming/design tools. They enable a holistic functional approach with the major advantage of setting up a unique modeling and evaluation environment for complete industrial electronics systems. Three main design rules are then presented. These are algorithm refinement, modularity, and systematic search for the best compromise between the control performance and the architectural constraints. An overview of contributions and limits of FPGAs is also given, followed by a short survey of FPGA-based intelligent controllers for modern industrial systems. Finally, two complete and timely case studies are presented to illustrate the benefits of an FPGA implementation when using the proposed system modeling and design methodology. These consist of the direct torque control for induction motor drives and the control of a diesel-driven synchronous stand-alone generator with the help of fuzzy logic.
••16 Feb 2004
TL;DR: A novel design methodology to implement a secure DPA resistant crypto processor that combines standard building blocks to make 'new' compound standard cells, which have a close to constant power consumption.
Abstract: This paper describes a novel design methodology to implement a secure DPA resistant crypto processor. The methodology is suitable for integration in a common automated standard cell ASIC or FPGA design flow. The technique combines standard building blocks to make 'new' compound standard cells, which have a close to constant power consumption. Experimental results indicate a 50 times reduction in the power consumption fluctuations.
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