Field-programmable gate array: Difference between revisions

Revision as of 15:47, 7 October 2024

Revision as of 17:04, 11 October 2024

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** [[Microsemi]] (previously [[Actel]]), producing antifuse, flash-based, [[mixed-signal]] FPGAs; acquired by Microchip in 2018

** [[Atmel]], a second source of some Altera-compatible devices; also FPSLIC{{Clarify|reason=|date=December 2018}} mentioned above;<ref>{{Cite news|url=http://sourcetech411.com/2013/04/top-fpga-companies-for-2013/|title=Top FPGA Companies For 2013|date=2013-04-28|work=SourceTech411|access-date=2018-12-01|language=en-US|archive-date=2018-08-24|archive-url=https://web.archive.org/web/20180824135219/https://sourcetech411.com/2013/04/top-fpga-companies-for-2013/|url-status=dead}}</ref> acquired by Microchip in 2016

* QuickLogic manufactures ultra-low-power sensor hubs, extremely-low-powered, low-density SRAM-based FPGAs, with display bridges MIPI and RGB inputs; MIPI, RGB and LVDS outputs.<ref>{{Cite web|url=http://www.quicklogic.com/|title=QuickLogic — Customizable Semiconductor Solutions for Mobile Devices|website=www.quicklogic.com|publisher=QuickLogic Corporation|language=en|access-date=2018-10-07}}{{better source needed|{{subst:DATE}}|date=September 2024}}</ref><!--[[User:Kvng/RTH]]-->

== Applications ==

{{See also|Hardware acceleration}}

An FPGA can be used to solve any problem which is [[computable]]. This is trivially proven by the fact that FPGAs can be used to implement a [[soft microprocessor]], such as the Xilinx [[MicroBlaze]] or Altera [[Nios II]]. TheirBut their advantage lies in that they are significantly faster for some applications because of their [[Parallel computing|parallel nature]] and [[Logic optimization|optimality]] in terms of the number of gates used for certain processes.<ref name="Xilinx-Inc-Apr-2006-8-K">{{cite web|url=http://edgar.secdatabase.com/657/110465906027899/filing-main.htm |title=Xilinx Inc, Form 8-K, Current Report, Filing Date Apr 26, 2006 |publisher=secdatabase.com |access-date =May 6, 2018}}</ref>

FPGAs were originally beganintroduced as competitors to [[Complex programmable logic device|CPLDs]] to implement [[glue logic]] for [[printed circuit board]]s. As their size, capabilities, and speed increased, FPGAs took over additional functions to the point where some are now marketed as full [[Systemsystems on a chip|systems on chips]]s (SoCs). Particularly with the introduction of dedicated [[Binary multiplier|multiplier]]s into FPGA architectures in the late 1990s, applications whichthat had traditionally been the sole reserve of [[digital signal processor|digital signal processor hardware]]s (DSPs) began to incorporateuse FPGAs instead.<ref>{{cite web|url=https://www.bdti.com/articles/info_eet0207fpga.htm|title=Publications and Presentations|work=bdti.com|access-date=2018-11-02|archive-url=https://web.archive.org/web/20100821182813/http://www.bdti.com/articles/info_eet0207fpga.htm|archive-date=2010-08-21|url-status=dead}}</ref><ref>{{cite web|url=https://www.eetimes.com/xilinx-aims-65-nm-fpgas-at-dsp-applications/#|title=Xilinx aims 65-nm FPGAs at DSP applications|work=EETimes|first=Mark|last=LaPedus|date=5 February 2007 }}</ref><!--[[User:Kvng/RTH]]-->

The evolution of FPGAs has motivated an increase in the use of these devices, whose architecture allows the development of hardware solutions optimized for complex tasks, such as 3D MRI image segmentation, 3D discrete wavelet transform, tomographic image reconstruction, or PET/MRI systems.<ref>{{Cite journal |last1=Alcaín |first1=Eduardo |last2=Fernández |first2=Pedro R. |last3=Nieto |first3=Rubén |last4=Montemayor |first4=Antonio S. |last5=Vilas |first5=Jaime |last6=Galiana-Bordera |first6=Adrian |last7=Martinez-Girones |first7=Pedro Miguel |last8=Prieto-de-la-Lastra |first8=Carmen |last9=Rodriguez-Vila |first9=Borja |last10=Bonet |first10=Marina |last11=Rodriguez-Sanchez |first11=Cristina |date=2021-12-15 |title=Hardware Architectures for Real-Time Medical Imaging |journal=Electronics |language=en |volume=10 |issue=24 |pages=3118 |doi=10.3390/electronics10243118 |issn=2079-9292|doi-access=free }}</ref><ref>{{Cite journal |last1=Nagornov |first1=Nikolay N. |last2=Lyakhov |first2=Pavel A. |last3=Valueva |first3=Maria V. |last4=Bergerman |first4=Maxim V. |date=2022 |title=RNS-Based FPGA Accelerators for High-Quality 3D Medical Image Wavelet Processing Using Scaled Filter Coefficients |journal=IEEE Access |volume=10 |pages=19215–19231 |doi=10.1109/ACCESS.2022.3151361 |s2cid=246895876 |issn=2169-3536|doi-access=free |bibcode=2022IEEEA..1019215N }}</ref> The developed solutions can perform intensive computation tasks with parallel processing, are dynamically reprogrammable, and have a low cost, all while meeting the hard real-time requirements associated with medical imaging.