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{{short description|British semiconductor company}} |
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{{Infobox company |
{{Infobox company |
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| name = Graphcore Limited |
| name = Graphcore Limited |
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| logo = |
| logo = Graphcore logo.png |
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| type = [[Privately-held company|Private]] |
| type = [[Privately-held company|Private]] |
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| founded = {{Start date and age|2016}} |
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| ⚫ | |||
| founders = {{ubl|Nigel Toon|Simon Knowles}} |
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| ⚫ | |||
| industry = [[Semiconductors]] |
| industry = [[Semiconductors]] |
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| hq_location_city = [[Bristol]] |
| hq_location_city = [[Bristol]] |
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| hq_location_country = [[United Kingdom]] |
| hq_location_country = [[United Kingdom]] |
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| products = IPU, Poplar |
| products = IPU, Poplar |
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| revenue = {{US$|2.7 million}} (2022)<ref name=reuters2023 /> |
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| ⚫ | |||
| net_income = {{US$|-205 million}} (2022)<ref name=reuters2023 /> |
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| num_employees = 494 (2023)<ref name=reuters2023>{{Cite news |last=Cherney |first=Max A. |date=5 October 2023 |title=Losses widen, cash needed at chip startup Graphcore, an Nvidia rival, filing shows |url=https://www.reuters.com/technology/losses-widen-cash-needed-chip-startup-graphcore-an-nvidia-rival-filing-2023-10-05/ |work=Reuters}}</ref> |
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'''Graphcore''' is a British [[semiconductor]] company that develops [[AI accelerator|accelerators for AI]] and [[machine learning]]. It |
'''Graphcore Limited''' is a British [[semiconductor]] company that develops [[AI accelerator|accelerators for AI]] and [[machine learning]]. It has introduced a massively parallel ''Intelligence Processing Unit'' (IPU) that holds the complete machine learning model inside the processor.<ref>{{cite news |
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| title = AI Chip Startup Shares Insights: "Very large" FinFET chip in the works at TSMC |
| title = AI Chip Startup Shares Insights: "Very large" FinFET chip in the works at TSMC |
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| author = Peter Clarke |
| author = Peter Clarke |
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| Line 17: | Line 23: | ||
| publisher = eetimes |
| publisher = eetimes |
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| date = 2016-11-01 |
| date = 2016-11-01 |
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| |
| access-date = 2017-08-02 |
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}}</ref> |
}}</ref> |
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==History== |
==History== |
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Graphcore was founded in 2016 by Simon Knowles and Nigel Toon.<ref>{{cite news |url=https://www.theguardian.com/business/2020/dec/29/uk-graphcore-valued-28bn-nvidia-artificial-intelligence |title=UK chipmaker Graphcore valued at $2.8bn after it raises $222m |date=2020-12-29 |work=[[The Guardian]] |last=Jolly |first=Jasper}}</ref> |
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Graphcore was founded in 2016 by Simon Knowles and Nigel Toon. |
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In the autumn of 2016, Graphcore secured a first funding round |
In the autumn of 2016, Graphcore secured a first funding round led by [[Robert Bosch GmbH|Robert Bosch]] Venture Capital. Other backers included [[Samsung]], [[Amadeus Capital Partners]], C4 Ventures, [[Draper Esprit]], [[Foundation Capital]], and [[Pitango]].<ref>{{cite news| title = AI chipmaker Graphcore raises $30 million to take on Intel| author = Arjun Kharpal| url = https://www.cnbc.com/2016/10/31/ai-chipmaker-graphcore-raises-30-million-to-take-on-intel-nvidia.html| newspaper = CNBC| date = 2016-10-31| access-date = 2017-07-31}}</ref><ref>{{cite news| title = UK chip start-up Graphcore raises £30m for take on AI giants| author = Madhumita Murgia| url = https://www.ft.com/content/053f9dea-9e98-11e6-891e-abe238dee8e2| newspaper = Financial Times| date = 2016-10-31| access-date = 2017-08-02}}</ref> |
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In July 2017, Graphcore secured a round B funding |
In July 2017, Graphcore secured a round B funding led by [[Atomico]],<ref>{{cite news| title = U.K. Chip Designer Graphcore Gets $30 Million to Fund Expansion| author = Jeremy Kahn and Ian King| url = https://www.bloomberg.com/news/articles/2017-07-20/u-k-chip-designer-graphcore-gets-30-million-to-fund-expansion| newspaper = Bloomberg| date = 2017-07-20| access-date = 2017-07-31}}</ref> which was followed a few months later by $50 million in funding from [[Sequoia Capital]].<ref>{{cite news|last1=Lynley|first1=Matthew|title=Graphcore raises $50M amid a flurry of AI chip activity |date = 2017-11-12| url=https://techcrunch.com/2017/11/12/graphcore-raises-50m-amid-a-flurry-of-ai-chip-activity/|access-date=2017-12-07 |work=TechCrunch|language=en}}</ref> |
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In December 2018, Graphcore closed its series D with $200 million raised at a $1.7 billion valuation, making the company a [[Unicorn (finance)|unicorn]]. Investors included Microsoft, Samsung and Dell Technologies.<ref>{{Cite web|url=https://techcrunch.com/2018/12/18/ai-chip-startup-graphcore-closes-200m-series-d-adds-bmw-and-microsoft-as-strategic-investors/|title=AI chip startup Graphcore closes $200M Series D, adds BMW and Microsoft as strategic investors|website=TechCrunch|language=en-US|access-date=2018-12-19}}</ref> |
In December 2018, Graphcore closed its series D with $200 million raised at a $1.7 billion valuation, making the company a [[Unicorn (finance)|unicorn]]. Investors included Microsoft, Samsung and Dell Technologies.<ref>{{Cite web|url=https://techcrunch.com/2018/12/18/ai-chip-startup-graphcore-closes-200m-series-d-adds-bmw-and-microsoft-as-strategic-investors/|title=AI chip startup Graphcore closes $200M Series D, adds BMW and Microsoft as strategic investors|website=TechCrunch|date=18 December 2018 |language=en-US|access-date=2018-12-19}}</ref> |
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On 13 November 2019, Graphcore announced that their Graphcore C2 IPUs |
On 13 November 2019, Graphcore announced that their Graphcore C2 IPUs were available for preview on [[Microsoft Azure]].<ref>{{Cite web|url=https://www.graphcore.ai/posts/microsoft-and-graphcore-collaborate-to-accelerate-artificial-intelligence|title=Microsoft and Graphcore collaborate to accelerate Artificial Intelligence|last=Toon|first=Nigel|website=www.graphcore.ai|language=en-gb|access-date=2019-11-16}}</ref> |
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[[Meta Platforms]] acquired the AI networking technology team from Graphcore in early 2023.<ref>{{cite news |last=Paul |first=Katie |date=5 May 2023 |title=Meta Platforms scoops up AI networking chip team from Graphcore |url=https://www.reuters.com/technology/meta-platforms-scoops-up-ai-networking-chip-team-graphcore-2023-05-05/ |publisher=Reuters}}</ref> |
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In July 2024, [[Softbank Group]] agreed to acquire Graphcore for around $500 million. The deal is under review by the [[Department for Business, Energy and Industrial Strategy|UK's Business Department's]] investment security unit.<ref>{{Cite web |last=Nicol-Schwarz |first=Kai |date=9 July 2024 |title=Graphcore employees have share value wiped as sale to SoftBank agreed |url=https://sifted.eu/articles/graphcore-conditional-sale-agreed-news |work=Sifted}}</ref><ref>{{Cite web |last1=Titcomb |first1=James |last2=Field |first2=Matthew |date=1 July 2024 |title=Japanese deal for AI champion Graphcore faces national security review |url=https://www.telegraph.co.uk/business/2024/07/01/softbank-ai-deal-graphcore-national-security-review/ |work=The Daily Telegraph}}</ref> |
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==Products== |
==Products== |
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In 2016, Graphcore announced the world's first graph tool chain designed for machine intelligence called Poplar Software Stack.<ref>{{Cite web|url=https://www.graphcore.ai/posts/what-does-machine-learning-look-like|title=Inside an AI 'brain' - What does machine learning look like?|last=Fyles|first=Matt|website=www.graphcore.ai|language=en-gb|access-date=2019-11-16}}</ref><ref>{{Cite web|url=https://www.graphcore.ai/posts/introducing_poplar_our_ipu_processor_software_at_neurips|title=Introducing Poplar® - our IPU-Processor software at NeurIPS|last=Doherty|first=Sally|website=www.graphcore.ai|language=en-gb|access-date=2019-11-16}}</ref><ref>{{Cite web|url=https://www.graphcore.ai/posts/graph-computing-for-machine-intelligence-with-poplar|title=Graph computing for machine intelligence with Poplar™|last=Fyles|first=Matt|website=www.graphcore.ai|language=en-gb|access-date=2019-11-16}}</ref> |
In 2016, Graphcore announced the world's first graph tool chain designed for machine intelligence called Poplar Software Stack.<ref>{{Cite web|url=https://www.graphcore.ai/posts/what-does-machine-learning-look-like|title=Inside an AI 'brain' - What does machine learning look like?|last=Fyles|first=Matt|website=www.graphcore.ai|language=en-gb|access-date=2019-11-16}}</ref><ref>{{Cite web|url=https://www.graphcore.ai/posts/introducing_poplar_our_ipu_processor_software_at_neurips|title=Introducing Poplar® - our IPU-Processor software at NeurIPS|last=Doherty|first=Sally|website=www.graphcore.ai|language=en-gb|access-date=2019-11-16}}</ref><ref>{{Cite web|url=https://www.graphcore.ai/posts/graph-computing-for-machine-intelligence-with-poplar|title=Graph computing for machine intelligence with Poplar™|last=Fyles|first=Matt|website=www.graphcore.ai|language=en-gb|access-date=2019-11-16}}</ref> |
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In July 2017, Graphcore announced |
In July 2017, Graphcore announced its first chip, called the Colossus GC2, a "16 nm massively parallel, mixed-precision floating point processor", that became available in 2018.<ref name="Tiffany">{{cite web|url=https://www.hpcwire.com/2017/07/20/graphcore-readies-launch-16nm-colossus-ipu-chip/|title=Graphcore Readies Launch of 16nm Colossus-IPU Chip |last1=Trader |first1=Tiffany |date=2017-07-20 |website=hpcwire.com |publisher=HPC Wire |access-date=2017-12-11}}</ref><ref name="Lucchesi">{{cite web|url=https://silvertonconsulting.com/blog/2018/11/19/new-graphcore-gc2-chips-with-2pflop-performance-in-a-dell-server/|title=New GraphCore GC2 chips with 2PFlop performance in a Dell Server |last1=Lucchesi |first1=Ray |date=2018-11-19 |website=silvertonconsulting.com |publisher=Silverton Consulting |access-date=2018-12-16}}</ref> Packaged with two chips on a single PCI Express card, called the Graphcore C2 IPU (an Intelligence Processing Unit), it is stated to perform the same role as a GPU in conjunction with standard machine learning frameworks such as [[TensorFlow]].<ref name=Tiffany/> The device relies on [[scratchpad memory]] for its performance rather than traditional cache hierarchies.<ref>{{cite web|url=https://www.graphcore.ai/hubfs/assets/pdf/Citadel%20Securities%20Technical%20Report%20-%20Dissecting%20the%20Graphcore%20IPU%20Architecture%20via%20Microbenchmarking%20Dec%202019.pdf|title=Dissecting the Graphcore IPU Architecture via Microbenchmarking|author=[[Citadel LLC|Citadel]] High Performance Computing R&D Team|date=2019}}</ref> |
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| ⚫ | In July 2020, Graphcore presented its second generation processor called GC200, built with [[TSMC]]'s [[7 nm process|7nm FinFET manufacturing process]]. GC200 is a 59 billion transistor, 823 square-millimeter integrated circuit with 1,472 computational cores and 900 Mbyte of local memory.<ref>{{Cite web|url=https://www.graphcore.ai/posts/introducing-second-generation-ipu-systems-for-ai-at-scale/|title=Graphcore Introducing 2nd Generation IPU Systems For AI At Scale|language=en-gb|access-date=2020-08-09}}</ref> In 2022, Graphcore and TSMC presented the ''Bow IPU'', a 3D package of a GC200 die bonded face to face to a power-delivery die that allows for higher clock rate at lower core voltage.<ref name="next">Timothy Prickett Morgan: [https://www.nextplatform.com/2022/03/03/graphcore-goes-3d-with-ai-chips-architects-10-exaflops-ultra-intelligent-machine/ ''GraphCore Goes Full 3D With AI Chips.''] The Next Platform, March 3, 2022.</ref> Graphcore aims at a ''Good machine'', named after [[I.J. Good]], enabling [[Artificial neural network|AI models]] with more parameters than the human brain has synapses.<ref name="next" /> |
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{| class="wikitable" |
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| ⚫ | In July 2020, Graphcore presented |
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|- |
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! Release date !! Product !! Process node !! Cores !! Threads !! Transistors |
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!tera[[FLOPS]] ([[FP16]]) |
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|- |
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| July 2017 || Colossus™ MK1 - GC2 IPU || 16 nm TSMC || 1216 || 7296 || ? |
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|~100-125<ref>{{Cite web |last=Kennedy |first=Patrick |date=2019-06-07 |title=Hands-on With a Graphcore C2 IPU PCIe Card at Dell Tech World |url=https://www.servethehome.com/hands-on-with-a-graphcore-c2-ipu-pcie-card-at-dell-tech-world/ |access-date=2023-06-26 |website=ServeTheHome |language=en-US}}</ref> |
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|- |
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| July 2020 || Colossus™ MK2 - GC200 IPU || 7 nm TSMC || 1472 || 8832 || 59 billion |
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|~250-280<ref>{{Cite web |last=Ltd |first=Graphcore |title=IPU Processors |url=https://www.graphcore.ai/products/ipu |access-date=2023-06-26 |website=www.graphcore.ai |language=en}}</ref> |
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|Colossus™ MK3 |
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|~500<ref>{{Cite web |title=ScalAH22: 13th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Heterogeneous Systems |url=https://www.csm.ornl.gov/srt/conferences/Scala/2022/ |access-date=2023-06-26 |website=www.csm.ornl.gov}}</ref> |
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Both the older and newer chips can use 6 threads per tile (for |
Both the older and newer chips can use 6 threads per tile{{what?|date=July 2024}} (for a total of 7,296 and 8,832 threads, respectively) "[[Multiple instruction, multiple data|MIMD]] (Multiple Instruction, Multiple Data) parallelism and has distributed, local memory as its only form of memory on the device" (except for registers).{{cn|date=July 2024}} The older GC2 chip has 256 KiB per tile while the newer GC200 chip has about 630 KiB per tile that are arranged into ''islands'' (4 tiles per island),<ref>{{Cite arXiv|title=Dissecting theGraphcore IPUArchitecturevia Microbenchmarking|eprint=1912.03413|last1=Jia|first1=Zhe|last2=Tillman|first2=Blake|last3=Maggioni|first3=Marco|author4=Daniele Paolo Scarpazza|year=2019|class=cs.DC }}</ref> that are arranged into columns, and latency is best within tile.{{what?|date=July 2024}}{{cn|date=July 2024}} The IPU uses IEEE [[Half-precision floating-point format|FP16]], with stochastic rounding, and also [[Single-precision floating-point format|single-precision FP32]], at lower performance.<ref>{{Cite web|title=THE GRAPHCORE SECOND GENERATION IPU|url=https://www.graphcore.ai/hubfs/MK2-%20The%20Graphcore%202nd%20Generation%20IPU%20Final%20v7.14.2020.pdf?hsLang=en}}</ref> Code and data executed locally must fit in a tile, but with message-passing, all on-chip or off-chip memory can be used, and software for AI makes it transparently possible,{{what?|date=July 2024}} e.g. has [[PyTorch]] support.{{cn|date=July 2024}} |
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==See also== |
==See also== |
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==References== |
==References== |
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{{ |
{{Reflist}} |
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==External links== |
==External links== |
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[[Category:British companies established in 2016]] |
[[Category:British companies established in 2016]] |
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[[Category:Companies based in Bristol]] |
[[Category:Companies based in Bristol]] |
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[[Category:Fabless semiconductor companies]] |
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[[Category:Announced mergers and acquisitions]] |
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Latest revision as of 11:29, 12 July 2024
 | |
| Company type | Private |
|---|---|
| Industry | Semiconductors |
| Founded | 2016 |
| Founders |
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| Headquarters | , |
Key people |
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| Products | IPU, Poplar |
| Revenue | US$2.7 million (2022)[1] |
| US$−205 million (2022)[1] | |
Number of employees | 494 (2023)[1] |
| Website | www |
Graphcore Limited is a British semiconductor company that develops accelerators for AI and machine learning. It has introduced a massively parallel Intelligence Processing Unit (IPU) that holds the complete machine learning model inside the processor.[2]
History
[edit]Graphcore was founded in 2016 by Simon Knowles and Nigel Toon.[3]
In the autumn of 2016, Graphcore secured a first funding round led by Robert Bosch Venture Capital. Other backers included Samsung, Amadeus Capital Partners, C4 Ventures, Draper Esprit, Foundation Capital, and Pitango.[4][5]
In July 2017, Graphcore secured a round B funding led by Atomico,[6] which was followed a few months later by $50 million in funding from Sequoia Capital.[7]
In December 2018, Graphcore closed its series D with $200 million raised at a $1.7 billion valuation, making the company a unicorn. Investors included Microsoft, Samsung and Dell Technologies.[8]
On 13 November 2019, Graphcore announced that their Graphcore C2 IPUs were available for preview on Microsoft Azure.[9]
Meta Platforms acquired the AI networking technology team from Graphcore in early 2023.[10]
In July 2024, Softbank Group agreed to acquire Graphcore for around $500 million. The deal is under review by the UK's Business Department's investment security unit.[11][12]
Products
[edit]In 2016, Graphcore announced the world's first graph tool chain designed for machine intelligence called Poplar Software Stack.[13][14][15]
In July 2017, Graphcore announced its first chip, called the Colossus GC2, a "16 nm massively parallel, mixed-precision floating point processor", that became available in 2018.[16][17] Packaged with two chips on a single PCI Express card, called the Graphcore C2 IPU (an Intelligence Processing Unit), it is stated to perform the same role as a GPU in conjunction with standard machine learning frameworks such as TensorFlow.[16] The device relies on scratchpad memory for its performance rather than traditional cache hierarchies.[18]
In July 2020, Graphcore presented its second generation processor called GC200, built with TSMC's 7nm FinFET manufacturing process. GC200 is a 59 billion transistor, 823 square-millimeter integrated circuit with 1,472 computational cores and 900 Mbyte of local memory.[19] In 2022, Graphcore and TSMC presented the Bow IPU, a 3D package of a GC200 die bonded face to face to a power-delivery die that allows for higher clock rate at lower core voltage.[20] Graphcore aims at a Good machine, named after I.J. Good, enabling AI models with more parameters than the human brain has synapses.[20]
| Release date | Product | Process node | Cores | Threads | Transistors | teraFLOPS (FP16) |
|---|---|---|---|---|---|---|
| July 2017 | Colossus™ MK1 - GC2 IPU | 16 nm TSMC | 1216 | 7296 | ? | ~100-125[21] |
| July 2020 | Colossus™ MK2 - GC200 IPU | 7 nm TSMC | 1472 | 8832 | 59 billion | ~250-280[22] |
| Colossus™ MK3 | ~500[23] |
Both the older and newer chips can use 6 threads per tile[clarification needed] (for a total of 7,296 and 8,832 threads, respectively) "MIMD (Multiple Instruction, Multiple Data) parallelism and has distributed, local memory as its only form of memory on the device" (except for registers).[citation needed] The older GC2 chip has 256 KiB per tile while the newer GC200 chip has about 630 KiB per tile that are arranged into islands (4 tiles per island),[24] that are arranged into columns, and latency is best within tile.[clarification needed][citation needed] The IPU uses IEEE FP16, with stochastic rounding, and also single-precision FP32, at lower performance.[25] Code and data executed locally must fit in a tile, but with message-passing, all on-chip or off-chip memory can be used, and software for AI makes it transparently possible,[clarification needed] e.g. has PyTorch support.[citation needed]
See also
[edit]
References
[edit]- ^ a b c Cherney, Max A. (5 October 2023). "Losses widen, cash needed at chip startup Graphcore, an Nvidia rival, filing shows". Reuters.
- ^ Peter Clarke (2016-11-01). "AI Chip Startup Shares Insights: "Very large" FinFET chip in the works at TSMC". eetimes. Retrieved 2017-08-02.
- ^ Jolly, Jasper (2020-12-29). "UK chipmaker Graphcore valued at $2.8bn after it raises $222m". The Guardian.
- ^ Arjun Kharpal (2016-10-31). "AI chipmaker Graphcore raises $30 million to take on Intel". CNBC. Retrieved 2017-07-31.
- ^ Madhumita Murgia (2016-10-31). "UK chip start-up Graphcore raises £30m for take on AI giants". Financial Times. Retrieved 2017-08-02.
- ^ Jeremy Kahn and Ian King (2017-07-20). "U.K. Chip Designer Graphcore Gets $30 Million to Fund Expansion". Bloomberg. Retrieved 2017-07-31.
- ^ Lynley, Matthew (2017-11-12). "Graphcore raises $50M amid a flurry of AI chip activity". TechCrunch. Retrieved 2017-12-07.
- ^ "AI chip startup Graphcore closes $200M Series D, adds BMW and Microsoft as strategic investors". TechCrunch. 18 December 2018. Retrieved 2018-12-19.
- ^ Toon, Nigel. "Microsoft and Graphcore collaborate to accelerate Artificial Intelligence". www.graphcore.ai. Retrieved 2019-11-16.
- ^ Paul, Katie (5 May 2023). "Meta Platforms scoops up AI networking chip team from Graphcore". Reuters.
- ^ Nicol-Schwarz, Kai (9 July 2024). "Graphcore employees have share value wiped as sale to SoftBank agreed". Sifted.
- ^ Titcomb, James; Field, Matthew (1 July 2024). "Japanese deal for AI champion Graphcore faces national security review". The Daily Telegraph.
- ^ Fyles, Matt. "Inside an AI 'brain' - What does machine learning look like?". www.graphcore.ai. Retrieved 2019-11-16.
- ^ Doherty, Sally. "Introducing Poplar® - our IPU-Processor software at NeurIPS". www.graphcore.ai. Retrieved 2019-11-16.
- ^ Fyles, Matt. "Graph computing for machine intelligence with Poplar™". www.graphcore.ai. Retrieved 2019-11-16.
- ^ a b Trader, Tiffany (2017-07-20). "Graphcore Readies Launch of 16nm Colossus-IPU Chip". hpcwire.com. HPC Wire. Retrieved 2017-12-11.
- ^ Lucchesi, Ray (2018-11-19). "New GraphCore GC2 chips with 2PFlop performance in a Dell Server". silvertonconsulting.com. Silverton Consulting. Retrieved 2018-12-16.
- ^ Citadel High Performance Computing R&D Team (2019). "Dissecting the Graphcore IPU Architecture via Microbenchmarking" (PDF).
- ^ "Graphcore Introducing 2nd Generation IPU Systems For AI At Scale". Retrieved 2020-08-09.
- ^ a b Timothy Prickett Morgan: GraphCore Goes Full 3D With AI Chips. The Next Platform, March 3, 2022.
- ^ Kennedy, Patrick (2019-06-07). "Hands-on With a Graphcore C2 IPU PCIe Card at Dell Tech World". ServeTheHome. Retrieved 2023-06-26.
- ^ Ltd, Graphcore. "IPU Processors". www.graphcore.ai. Retrieved 2023-06-26.
- ^ "ScalAH22: 13th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Heterogeneous Systems". www.csm.ornl.gov. Retrieved 2023-06-26.
- ^ Jia, Zhe; Tillman, Blake; Maggioni, Marco; Daniele Paolo Scarpazza (2019). "Dissecting theGraphcore IPUArchitecturevia Microbenchmarking". arXiv:1912.03413 [cs.DC].
- ^ "THE GRAPHCORE SECOND GENERATION IPU" (PDF).