AI Chip Startup Graphcore Lands $50 Million in Funding

Release time:2017-11-15
author:Ameya360
source: Dylan McGrath
reading:1417

  Graphcore, a developer of processors for machine learning and artificial intelligence, secured $50 million in additional funding, bringing the total raised by the UK-based startup to about $110 million over the past 18 months.

  Graphcore's series C funding, provided by venture firm Sequoia Capital, will be used to scale up production of the startup's first chip, which it calls an Intelligence Processing Unit (IPU). Graphcore plans to make the IPU available to early access customers at the beginning of next year.

  In addition to scaling up production, the new funding will be used to help build a community of developers around Graphcore's Poplar software platform, driving the company's extended product roadmap and investing in its Palo Alto, Calif.-based U.S. team to help support customers, Graphcore (Bristol, U.K.) said.

  "Efficient AI processing power is rapidly becoming the most sought-after resource in the technological world," said Nigel Toon, Graphcore's CEO, in a press statement. "We believe our IPU technology will become the worldwide standard for machine intelligence compute."

  Graphcore, which is featured in the most recent edition of EE Times Silicon 60, is perhaps the furthest along of a crop of startups that have been formed to create new processor architectures for deep neural networks (DNNs). In addition to Graphcore, other well-funded startups include Wave Computing, Cerebras and Groq.

  These startups and others are in the early days of battle with the likes of more established companies such as Google, which has offered its Tensor Processing Unit (TPU) custom ASIC for machine learning since last year, Nvidia GPUs and Intel, which acquired Nervana and plans to sample its Neural Network Processor ASSP next year.

  Toon maintains that the performance of Graphcore’s processor "is going to be transformative" compared to other accelerators. The company last month shared preliminary benchmarks that it says demonstrate that the IPU can improve performance of machine intelligence training and inference workloads by 10-100 times compared with current hardware.

  Previous investors in Graphcore include both Samsung Catalyst Fund, the venture capital arm of Samsung, and Robert Bosch Venture Capital.

  Matt Miller, a partner at Sequoia, will join Graphcore's board of directors as the result of the funding, the company said. Bill Coughran, another partner at Sequoia, will join Graphcore's technical advisory board, the company said.

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AI Chip Rides Novel Networks
  An emerging company with a novel machine-learning technology and equally unique financial structure debuted its first hardware product today. BrainChip rolled out an FPGA-based accelerator for its spiking neural network (SNN) software and hopes to deliver an ASIC within two years to expand its existing markets.  SNNs are related but different from the convolutional neural nets (CNNs) now widely used and, some say, hyped by web giants for jobs like voice and image recognition. SNNs use a simpler, one-shot training method and are well-suited to tasks such as face recognition in low-resolution and noisy environments such as surveillance video. To date, BrainChip’s products are used mainly by law enforcement and security.  The BrainChip accelerator packs six SNN cores in a Xilinx Kintex chip on a PCI Express board processing video at up to 600 frames/s at about 15 W max. It provides as much as a six-fold performance boost for the BrainChip Studio software for x86 computers that the company rolled out in July at a cost starting at $4,000 per video channel. The company first described its architecture in late 2015.  Today’s emerging CNN chips essentially accelerate sparse linear algebra to shorten training loops. By contrast, BrainChip’s accelerator speeds processes in digital pathways said to mimic neural synapses, reinforcing or inhibiting traffic and setting thresholds as appropriate.  The card will be available at the end of September at a $10,000 list price for single units. The company will sell the card to system integrators. It also may sell integrated bundles of cards, software, and servers directly to end users.  The company claims that the card is the first commercial hardware to accelerate SNNs. IBM’s True North is more widely known but has been more of a general-purpose research vehicle, although the U.S. Air Force said in June that it would use it in a supercomputer. Stanford and the European Union also support research efforts in SNN accelerators.  BrainChip licensed technology for an ASIC to accelerate unsupervised learning in SNNs from a research group in Toulouse, France. The company is studying the potential in automotive, cybersecurity, financial, and medical markets to determine how to tailor the silicon that could be available in 12 to 24 months.  The company got its start 10 years ago as a spinout from the Toulouse University research effort that was creating custom software for users in France. BrainChip now consists of a software team in Toulouse, a hardware group in southern California, and last year, it brought on new management mainly in Silicon Valley.  A co-founder from Australia balked at financial terms of traditional venture capitalists. As an alternative, the team engineered a reverse takeover of an underactive mining company in Australia and raised $15 million from public investors on the Australian stock market, where it is now listed as a small-cap stock.  The company would not comment on plans for further fundraising, which it will clearly need to fund ASIC development while its still-meager software and card revenues slowly expand.
2017-09-13 00:00 reading:1315
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