Tech News

Global AI market predicted to reach nearly $1 trillion by 2028

The financial outlook for the global artificial intelligence market looks pretty good in 2021. But that’s nothing compared to the expansion experts expect to see over the next seven years.

According to a recently released analysis report from Grand View Research, the AI market is expected to reach nearly $100 billion in 2021 and nearly ten times that by 2028.

Per a report from Grand View Research:

The global AI market is expected to grow at a compound annual growth rate of 40.2% from 2021 to 2028 to reach USD 997.77 billion by 2028.

This is based on numerous data points, but the most indicative might be the year-over-year growth we’re seeing from 2020 (where the market was evaluated at approximately $60 billion) to this year’s predicted evaluation of just shy of $100 billion.

[Read: Why entrepreneurship in emerging markets matters]

Driving the growth is mass scale adoption of deep learning techniques and a huge uptick in artificial intelligence hardware production and adoption.

According to a report from CNBC, early movers in the AI hardware space have been handsomely rewarded and the gravy train may just be getting started. A different analyst organization, Oppenheimer, sees the AI hardware market breaking the $100 billion mark by 2025.

Quick take: There’s an endless upside when it comes to artificial intelligence. In our research we haven’t been able to find a single expert analyst who’s sour on the future of AI.

While the future of many startups – especially those who rely on privacy-invasive systems – may be in jeopardy thanks to upcoming EU regulation, the simple fact of the matter is that strong AI is a steadily rising commodity that’s become a living necessity on par with running water and electricity.

Simply put, businesses around the globe run on AI.

While we don’t offer investment advice here at Neural (we’re enthusiasts, journalists, and science lovers but not financial experts), it’s safe to say artificial intelligence probably has a bright future in the global marketplace.

Repost: Original Source and Author Link


Cerebras launches new AI supercomputing processor with 2.6 trillion transistors

Join Transform 2021 this July 12-16. Register for the AI event of the year.

Cerebras Systems has unveiled its new Wafer Scale Engine 2 processor with a record-setting 2.6 trillion transistors and 850,000 AI-optimized cores. It’s built for supercomputing tasks, and it’s the second time since 2019 that Los Altos, California-based Cerebras has unveiled a chip that is basically an entire wafer.

Chipmakers normally slice a wafer from a 12-inch-diameter ingot of silicon to process in a chip factory. Once processed, the wafer is sliced into hundreds of separate chips that can be used in electronic hardware.

But Cerebras, started by SeaMicro founder Andrew Feldman, takes that wafer and makes a single, massive chip out of it. Each piece of the chip, dubbed a core, is interconnected in a sophisticated way to other cores. The interconnections are designed to keep all the cores functioning at high speeds so the transistors can work together as one.

Twice as good as the CS-1

Above: Comparing the CS-1 to the biggest GPU.

Image Credit: Cerebras

In 2019, Cerebras could fit 400,000 cores and 1.2 billion transistors on a wafer chip, the CS-1. It was built with a 16-nanometer manufacturing process. But the new chip is built with a high-end 7-nanometer process, meaning the width between circuits is seven billionths of a meter. With such miniaturization, Cerebras can cram a lot more transistors in the same 12-inch wafer, Feldman said. It cuts that circular wafer into a square that is eight inches by eight inches, and ships the device in that form.

“We have 123 times more cores and 1,000 times more memory on chip and 12,000 times more memory bandwidth and 45,000 times more fabric bandwidth,” Feldman said in an interview with VentureBeat. “We were aggressive on scaling geometry, and we made a set of microarchitecture improvements.”

Now Cerebras’ WSE-2 chip has more than twice as many cores and transistors. By comparison the largest graphics processing unit (GPU) has only 54 billion transistors — 2.55 trillion fewer transistors than the WSE-2. The WSE-2 also has 123 times more cores and 1,000 times more high performance on-chip high memory than GPU competitors. Many of the Cerebras cores are redundant in case one part fails.

“This is a great achievement, especially when considering that the world’s third largest chip is 2.55 trillion transistors smaller than the WSE-2,” said Linley Gwennap, principal analyst at The Linley Group, in a statement.

Feldman half-joked that this should prove that Cerebras is not a one-trick pony.

“What this avoids is all the complexity of trying to tie together lots of little things,” Feldman said. “When you have to build a cluster of GPUs, you have to spread your model across multiple nodes. You have to deal with device memory sizes and memory bandwidth constraints and communication and synchronization overheads.”

The CS-2’s specs

Above: TSMC put the CS-1 in a chip museum.

Image Credit: Cerebras

The WSE-2 will power the Cerebras CS-2, the industry’s fastest AI computer, designed and optimized for 7 nanometers and beyond. Manufactured by contract manufacturer TSMC, the WSE-2 more than doubles all performance characteristics on the chip — the transistor count, core count, memory, memory bandwidth, and fabric bandwidth — over the first generation WSE. The result is that on every performance metric, the WSE-2 is orders of magnitude larger and more performant than any competing GPU on the market, Feldman said.

TSMC put the first WSE-1 chip in a museum of innovation for chip technology in Taiwan.

“Cerebras does deliver the cores promised,” Patrick Moorhead, an analyst at Moor Insights & Strategy. “What the company is delivering is more along the lines of multiple clusters on a chip. It does appear to give Nvidia a run for its money but doesn’t run raw CUDA. That has become somewhat of a de facto standard. Nvidia solutions are more flexible as well as they can fit into nearly any server chassis.”

With every component optimized for AI work, the CS-2 delivers more compute performance at less space and less power than any other system, Feldman said. Depending on workload, from AI to high-performance computing, CS-2 delivers hundreds or thousands of times more performance than legacy alternatives, and it does so at a fraction of the power draw and space.

A single CS-2 replaces clusters of hundreds or thousands of graphics processing units (GPUs) that consume dozens of racks, use hundreds of kilowatts of power, and take months to configure and program. At only 26 inches tall, the CS-2 fits in one-third of a standard datacenter rack.

“Obviously, there are companies and entities interested in Cerebras’ wafer-scale solution for large data sets,” said Jim McGregor, principal analyst at Tirias Research, in an email. “But, there are many more opportunities at the enterprise level for the millions of other AI applications and still opportunities beyond what Cerebras could handle, which is why Nvidia has the SuprPod and Selene supercomputers.”

He added, “You also have to remember that Nvidia is targeting everything from AI robotics with Jenson to supercomputers. Cerebras is more of a niche platform. It will take some opportunities but will not match the breadth of what Nvidia is targeting. Besides, Nvidia is selling everything they can build.”

Lots of customers

Above: Comparing the new Cerebras chip to its rival, the Nvidia A100.

Image Credit: Cerebras

And the company has proven itself by shipping the first generation to customers. Over the past year, customers have deployed the Cerebras WSE and CS-1, including Argonne National Laboratory; Lawrence Livermore National Laboratory; Pittsburgh Supercomputing Center (PSC) for its Neocortex AI supercomputer; EPCC, the supercomputing center at the University of Edinburgh; pharmaceutical leader GlaxoSmithKline; Tokyo Electron Devices; and more. Customers praising the chip include those at GlaxoSmithKline and the Argonne National Laboratory.

Kim Branson, senior vice president at GlaxoSmithKline, said in a statement that the company has increased the complexity of the encoder models it generates while decreasing training time by 80 times. At Argonne, the chip is being used for cancer research and has reduced the experiment turnaround time on cancer models by more than 300 times.

“For drug discovery, we have other wins that we’ll be announcing over the next year in heavy manufacturing and pharma and biotech and military,” Feldman said.

The new chips will ship in the third quarter. Feldman said the company now has more than 300 engineers, with offices in Silicon Valley, Toronto, San Diego, and Tokyo.


VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Repost: Original Source and Author Link

Tech News

Bitcoin reaches new milestone with $1 trillion market cap

Bitcoin has seen a surge in recent weeks, sitting with a value of around $54,000 as of Friday, February 19. The cryptocurrency has reached a $1 trillion market capitalization. The milestone follows Tesla’s recently revealed $1.5 billion Bitcoin purchase, as well as tweets about the cryptocurrency from Elon Musk, which helped fuel public interest in the volatile coins.

Bitcoin, the number one cryptocurrency, experienced its first major spike in value back in 2018 before dipping slowly, only to resurge in the latter half of 2019. Growth has had ups and downs since then, with the value of a digital coin once again climbing significantly in recent months — and spiking drastically over the past few weeks.

According to CoinMarketCap, bitcoin is currently worth nearly $55,000 per coin, pushing its market cap past the $1 trillion mark. The cryptocurrency has grown significantly since its official launch in January 2009, gradually earning its place as a valid currency for buying goods from select major companies.

Bitcoin retains its fair share of adherents and critics, with mainstream interest in the cryptocurrency growing. The average consumer can now easily acquire their own bitcoin using ATMs through which physical cash is paid — some of which also support selling your bitcoin to get cash in return.

At this point in time, bitcoin dominates around 60-percent of the market, at least among the cryptocurrencies tracked by CoinMarketCap. Though many remain skeptical about the cryptocurrency, a number of large entities are adopting bitcoin, including the Bank of New York Mellon, which made its announcement earlier this month.

Repost: Original Source and Author Link