Field-programmable gate array: Difference between revisions

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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.

Another trend in the use of FPGAs is [[hardware acceleration]], where one can use the FPGA to accelerate certain parts of an algorithm and share part of the computation between the FPGA and a general-purpose processor. The search engine [[Bing (search engine)|Bing]] is noted for adopting FPGA acceleration for its search algorithm in 2014.<ref name="BingFPGA">{{cite news |last1=Morgan |first1=Timothy Pricket |title=How Microsoft Is Using FPGAs To Speed Up Bing Search |url=https://www.enterprisetech.com/2014/09/03/microsoft-using-fpgas-speed-bing-search/ |access-date=2018-09-18 |publisher=Enterprise Tech |date=2014-09-03}}</ref> {{as of|2018}}, FPGAs are seeing increased use as [[AI accelerator]]s including Microsoft's Project Catapult<ref name="ProjCatapult">{{cite web|url=https://www.microsoft.com/en-us/research/project/project-catapult/|title=Project Catapult|date=July 2018|website=Microsoft Research}}</ref> and for accelerating [[artificial neural network]]s for [[machine learning]] applications.<!--[[User:Kvng/RTH]]-->

TraditionallyOriginally,{{When|date=October 2018}} FPGAs have beenwere reserved for specific [[vertical application]]s where the volume of production is small. For these low-volume applications, the premium that companies pay in hardware cost per unit for a programmable chip is more affordable than the development resources spent on creating an ASIC. {{AsOften a custom-made chip would be cheaper if made in larger quantities, but FPGAs may be chosen to quickly bring a product to market. By of|2017}}, new cost and performance dynamics have broadened the range of viable applications.{{cn|date=December 2024}}<!--[[User:Kvng/RTH]]-->

Where personal computer peripherals exist in niche markets or are struggling to make inroads into a mass market (sometimes despite heavy promotion), it can be more cost-effective to utilise FPGAs for small production runs (e.g. 1,000 units). Examples include exotic products such as e.g. [[ArVid]], a VHS tape archiver (only some versions of which were FPGA-based) and [[Gigabyte Technology]]'s [[i-RAM]] budget pseudo-[[SSD drive]], which used a Xilinx FPGA.<ref>{{Cite web|date=2005-07-25|title=Gigabyte's i-RAM: Affordable Solid State Storage|url=https://www.anandtech.com/show/1742/2|access-date=2020-12-16|website=anandtech.com|language=en-US}}</ref> Often a custom-made chip would be cheaper if made in larger quantities, but FPGAs may be chosen to quickly bring a product to market. Again, to the extent the availability of lower-cost FPGAs is increasing, it can become justifiable to include them even in larger production runs.

Other uses for FPGAs include: