Karl Freund, Patrick Moorhead

Why Can’t NVIDIA Be Bested In MLPerf?

MLPerf, an industry consortium of over 70 companies and institutions, has released the second round of AI Inference processing results. These benchmarks now represent production applications from all major areas of AI deployment today. But only a few technologies participated in the contest. Many observers and investors expect NVIDIA to encounter more competition in the market for inference accelerators. The hypothesis is that GPUs consume too much energy, are expensive, and cannot …

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The 2020 AI HW Summit: Beware The Benchmarks

The 3rd annual AI Hardware Summit concluded last week, after four days of mind-bending presentations, panels and discussions about the Cambrian Explosion in AI. For well over two years, many of us industry observers have waited for the players, big and small, to present working production silicon with benchmarks. In previous years, only NVIDIA delivered (or should …

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NVIDIA GTC: “DPU” Smart NIC And More

NVIDIA Co-founder and CEO Jensen Huang rarely disappoints his audience nor his investors. This week he once again delivered the goods at the GPU Technology Conference. Announcing a broad range of hardware and software innovations, Jensen made it clear that he intends to reshape computation, from GPUs to CPUs to NICs and switches. So, let’s …

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RESEARCH PAPER: Tenstorrent’s Holistic Stack Of AI Innovation

The explosive growth of AI processing in data center and edge environments has induced AI startups and established firms alike to develop silicon to handle the massive processing demands of neural networks. Inference processing, in particular, is an emerging opportunity, wherein a trained deep neural network is processed to predict characteristics of new data samples. …

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RESEARCH PAPER: Blaize: AI For The Edge

While NVIDIA dominates the market for AI-specific silicon accelerating the training of neural networks, many AI startups are developing silicon to accelerate inference processing, both for data center and edge applications. CPUs have typically been the choice for inference processing, but this is changing rapidly as the size of neural networks grows exponentially and applications …

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Qualcomm Launches Cloud AI Chip

Last year, Qualcomm teased its Cloud AI100, promising strong performance and power efficiency to enable Artificial Intelligence in cloud edge computing, autonomous vehicles and 5G infrastructure. Today, the company announced it is now sampling the platform, with volume shipments planned for the first half of 2021. This begs the question: why would a company known for …

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NVIDIA Needed A CPU, But Did It Need To Buy Arm To Get One?

I often opine that NVIDIA needs a data center-class CPU to compete with Intel and AMD, both of whom have used tightly-coupled CPU/GPU technology to win the first three U.S. exascale supercomputer deals. Connecting massive GPUs to fast CPUs over a painfully slow PCIe interface will not meet the needs of the future of supercomputing …

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NVIDIA Provides More Details On Selene Supercomputer

Last May, when NVIDIA unveiled the Ampere GPU architecture, the company announced a new supercomputer named Selene that ranks #7 in the world in total performance. Selene is now the fastest industrial system in the USA and is the second-most energy-efficient system ever built. The air-cooled Selene was constructed in a standard data center in …

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Blaize AI: Now In Production And Trials

Last November I covered Blaize and its silicon and software strategy, and noted that the company’s fairly large team has been focused on early customer engagements to gain insights and accelerate adoption. Now the company, backed by industrial heavyweights such as Samsung, Daimler and Denso, is advancing from development into full production and customer deployment. Blaize …

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NVIDIA AI Runs The Mlperf Table Again

Today, the industry standard AI benchmarking group, mlperf, released its 3rd raft of submissions for training AI networks, and just like the first two releases, NVIDIA swept a sparse competitive field in the category of commercially available hardware and software. Mlperf, comprised of some 80 companies and universities around the world, creates benchmarks to provide …

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Could Graphcore’s Second Chip Challenge NVIDIA?

Graphcore, a UK-based startup, launched its first Intelligence Processing Unit (IPU) for AI acceleration in 2018. Today it introduced its second-generation product for AI, a massively parallel chip with 59.4 billion transistors that delivers some 250 Trillion Operations per Second (TOPS). However, the company’s potential to challenge NVIDIA, the leader in data center AI, lies …

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RESEARCH PAPER: The Graphcore Second-Generation IPU

Graphcore, the U.K.-based startup that launched the Intelligence Processing Unit (IPU) for AI acceleration in 2018, has introduced the IPU-Machine. This second-generation platform has greater processing power, more memory and built-in scalability for handling extremely large parallel processing workloads. The well-funded startup has a blue-ribbon pedigree of engineers, advisers and investors, and enjoys a valuation …

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Intel’s New Chips Focus On AI

Today, Intel launched and disclosed new technologies across its portfolio of processors, with special emphasis on enhanced AI capabilities. Unlike its many competitors, who either produce a CPU, a GPU, an FPGA or an AI-specific accelerator, Intel’s strategy is “all of the above.” The company offers customers a range of solutions, from general purpose to …

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Does NVIDIA Selene Form A Wider Moat Than CUDA?

The annual International Supercomputer Conference (ISC), held virtually this year, kicked off today. Not surprisingly, NVIDIA has already made a few announcements of note. Especially of interest to me was the announcement of Selene, NVIDIA’s in-house 1+ Exaflop AI supercomputer, which ranks as the fastest industrial system in the USA and #7 overall in the …

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A Look At Graphcore’s AI Software

Software for new processor designs is critical to enabling application deployment and optimizing performance. UK-based startup Graphcore, the unicorn provider of silicon for application acceleration, places significant emphasis on software, dedicating roughly half its engineering staff to the challenge. Graphcore’s Intelligence Processing Unit (IPU) utilizes the expression of an algorithm as a directed graph, and …

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Microsoft Builds Massive Supercomputer For OpenAI, But Whose Chips Are Inside?

Microsoft has announced that the company has built a top 5 AI supercomputer for OpenAI, hosted in the Azure cloud. Microsoft invested a billion dollars in the OpenAI industry research group in 2019. The massive system is comprised of some 10,000 GPUs and over 285,000 CPU cores and will be used to advance the industry’s …

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NVIDIA Launches Ampere A100 GPU For Data Center Computing And AI

While many of us missed watching Jensen Huang on stage in his trademark leather jacket, he did not disappoint his on-line audience at the virtual GTC Keynote this week. In a session lasting over two and a half hours, the CEO and founder of NVIDIA announced new hardware to fend off a slew of potential …

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RESEARCH PAPER: The Graphcore Software Stack: Built To Scale

Software for new processor designs is critical to enabling application deployment and optimizing performance. UK-based startup Graphcore, a provider of silicon for application acceleration, places significant emphasis on software, dedicating roughly half its engineering staff to the challenge. Graphcore’s Intelligence Processing Unit (IPU) utilizes the expression of an algorithm as a directed graph, and the company’s Poplar software stack …

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Putting HPC To Work To Accelerate COVID-19 Research

The US arsenal of supercomputers has officially been opened up for COVID-19 research. The White House recently announced the new COVID-19 High Performance Computing Consortium, which will allow researchers worldwide to access to the world’s most powerful HPC resources and leverage them to combat the novel coronavirus. The White House believes that these high performance computing …

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