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Graphcore Limited
Company type	Private
Industry	Semiconductors
Founded	2016; 8 years ago
Founders	Nigel Toon; Simon Knowles;
Headquarters	Bristol, United Kingdom
Key people	Nigel Toon (CEO); Simon Knowles (CTO);
Products	IPU, Poplar
Revenue	US$2.7 million (2022)
Net income	US$−205 million (2022)
Number of employees	494 (2023)
Website	www.graphcore.ai

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

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

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]}