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Virtex

About: Virtex is a research topic. Over the lifetime, 2410 publications have been published within this topic receiving 33577 citations.


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Proceedings ArticleDOI
24 Feb 2002
TL;DR: The dynamic power consumption in the fabric of Field Programmable Gate Arrays (FPGAs) is analyzed by taking advantage of both simulation and measurement, and it is concluded that dynamic power dissipation of a Virtex-II CLB is 5.9μW per MHz for typical designs, but it may vary significantly depending on the switching activity.
Abstract: This paper analyzes the dynamic power consumption in the fabric of Field Programmable Gate Arrays (FPGAs) by taking advantage of both simulation and measurement. Our target device is Xilinx Virtex™-II family, which contains the most recent and largest programmable fabric. We identify important resources in the FPGA architecture and obtain their utilization, using a large set of real designs. Then, using a number of representative case studies we calculate the switching activity corresponding to each resource. Finally, we combine effective capacitance of each resource with its utilization and switching activity to estimate its share of power consumption. According to our results, the power dissipation share of routing, logic and clocking resources are 60%, 16%, and 14%, respectively. Also, we concluded that dynamic power dissipation of a Virtex-II CLB is 5.9mW per MHz for typical designs, but it may vary significantly depending on the switching activity.

439 citations

Journal ArticleDOI
TL;DR: A microarchitecture based on reconfigurable hardware emulation to allow high-speed reconfiguration and execution of the processor and to prove the viability of the proposal, the proposal was experimented with the MPEG-2 encoder and decoder and a Xilinx Virtex II Pro FPGA.
Abstract: In this paper, we present a polymorphic processor paradigm incorporating both general-purpose and custom computing processing. The proposal incorporates an arbitrary number of programmable units, exposes the hardware to the programmers/designers, and allows them to modify and extend the processor functionality at will. To achieve the previously stated attributes, we present a new programming paradigm, a new instruction set architecture, a microcode-based microarchitecture, and a compiler methodology. The programming paradigm, in contrast with the conventional programming paradigms, allows general-purpose conventional code and hardware descriptions to coexist in a program: In our proposal, for a given instruction set architecture, a onetime instruction set extension of eight instructions, is sufficient to implement the reconfigurable functionality of the processor. We propose a microarchitecture based on reconfigurable hardware emulation to allow high-speed reconfiguration and execution. To prove the viability of the proposal, we experimented with the MPEG-2 encoder and decoder and a Xilinx Virtex II Pro FPGA. We have implemented three operations, SAD, DCT, and IDCT. The overall attainable application speedup for the MPEG-2 encoder and decoder is between 2.64-3.18 and between 1.56-1.94, respectively, representing between 93 percent and 98 percent of the theoretically obtainable speedups.

436 citations

Journal ArticleDOI
25 Jul 2005
TL;DR: It is shown that reconfigurable computing designs are capable of achieving up to 500 times speedup and 70% energy savings over microprocessor implementations for specific applications.
Abstract: Reconfigurable computing is becoming increasingly attractive for many applications. This survey covers two aspects of reconfigurable computing: architectures and design methods. The paper includes recent advances in reconfigurable architectures, such as the Alters Stratix II and Xilinx Virtex 4 FPGA devices. The authors identify major trends in general-purpose and special-purpose design methods. It is shown that reconfigurable computing designs are capable of achieving up to 500 times speedup and 70% energy savings over microprocessor implementations for specific applications.

414 citations

Proceedings ArticleDOI
20 Jun 2011
TL;DR: A scalable dataflow hardware architecture optimized for the computation of general-purpose vision algorithms — neuFlow — and a dataflow compiler — luaFlow — that transforms high-level flow-graph representations of these algorithms into machine code for neu Flow are presented.
Abstract: In this paper we present a scalable dataflow hardware architecture optimized for the computation of general-purpose vision algorithms — neuFlow — and a dataflow compiler — luaFlow — that transforms high-level flow-graph representations of these algorithms into machine code for neuFlow. This system was designed with the goal of providing real-time detection, categorization and localization of objects in complex scenes, while consuming 10 Watts when implemented on a Xilinx Virtex 6 FPGA platform, or about ten times less than a laptop computer, and producing speedups of up to 100 times in real-world applications. We present an application of the system on street scene analysis, segmenting 20 categories on 500 × 375 frames at 12 frames per second on our custom hardware neuFlow.

407 citations

Proceedings ArticleDOI
07 Nov 2013
TL;DR: It is shown that the effects of the memory bottleneck can be reduced by a flexible memory hierarchy that supports the complex data access patterns in CNN workload and ensures that on-chip memory size is minimized, which reduces area and energy usage.
Abstract: In the near future, cameras will be used everywhere as flexible sensors for numerous applications. For mobility and privacy reasons, the required image processing should be local on embedded computer platforms with performance requirements and energy constraints. Dedicated acceleration of Convolutional Neural Networks (CNN) can achieve these targets with enough flexibility to perform multiple vision tasks. A challenging problem for the design of efficient accelerators is the limited amount of external memory bandwidth. We show that the effects of the memory bottleneck can be reduced by a flexible memory hierarchy that supports the complex data access patterns in CNN workload. The efficiency of the on-chip memories is maximized by our scheduler that uses tiling to optimize for data locality. Our design flow ensures that on-chip memory size is minimized, which reduces area and energy usage. The design flow is evaluated by a High Level Synthesis implementation on a Virtex 6 FPGA board. Compared to accelerators with standard scratchpad memories the FPGA resources can be reduced up to 13× while maintaining the same performance. Alternatively, when the same amount of FPGA resources is used our accelerators are up to 11× faster.

361 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202339
202254
202150
202057
201970
201878