Graphics chip giant Nvidia mopped up the floor with its competition in a benchmark set of tests released Wednesday afternoon, demonstrating better performance on a host of artificial intelligence tasks. The benchmark, called MLPerf, announced by the MLPerf organization, an industry consortium that administers the tests, showed Nvidia getting better speed on a variety of tasks that use neural networks, from categorizing images to recommending which products a person might like. Predictions are the part of AI where a trained neural network produces output on real data, as opposed to the training phase when the neural network system is first being refined. Benchmark results on training tasks were announced by MLPerf back in July. Many of the scores on the test results pertain to Nvidia's T4 chip that has been in the market for some time, but even more impressive results were reported for its A100 chips unveiled in May.
Most of you are probably familiar with the chip giants like Intel & AMD which command a bigger share of the computing processor market, but this entrant to the chip market in 1993 has solidified its reputation as a big name in the arena. Although most well-known for its graphical processing units (GPUs) -- GeForce is its primary & most popular product line, the company also provides system-on-a-chip units (SoCs) for the mobile computing and automotive market. Since 2014, Nvidia has begun to diversify its business from the niche markets of gaming, automotive electronics, and mobile devices. It is now venturing into the futuristic AI, along with providing parallel processing capabilities to researchers and scientists that allow them to efficiently run high-performance applications. Let's review of some these endeavors.
NVIDIA is sitting pretty in AI (artificial intelligence) right now. For the next few years, most AI systems will continue to be trained on NVIDIA GPUs and specialized hardware and cloud services that incorporate these processors. However, NVIDIA has been frustrated in its attempts to become a dominant provider of AI chips for deployment into smartphones, embedded systems, and other edge devices. To address that strategic gap, NVIDIA this past week announced that it is acquiring processor architecture firm Arm Holdings from SoftBank Group and the SoftBank Vision Fund. Once the acquisition closes in the expected 18 months, NVIDIA will retain Arm's name, brand identity, management team, and base of operations in Cambridge, United Kingdom.
In recent years, Nvidia has ridden one of the biggest waves in technology, selling chips needed to build increasingly clever artificial intelligence algorithms. Now, the company plans to catch another big swell--mobile computing--with a $40 billion acquisition of Arm, which designs the chips found in virtually all smartphones. The deal would reshape the chip industry overnight--putting Nvidia at the center of much of the action. But it will face regulatory scrutiny in the UK, Europe, the US, and China, because it would give Nvidia control over the chip blueprints used by multiple tech companies, including its competitors. The news may be especially concerning in China, since it could allow the US to restrict access to chip designs used in a wide array of products.
Jensen Huang is a big idea guy. He transformed a sleepy computer graphics processor company he founded into an artificial intelligence leader. Now he wants to bring AI everywhere. Nvidia ((NVDA) -Get Report) Monday announced that the company will acquire ARM Holdings for $40 billion in cash and stock. ARM is a designer of the low power microchip architecture used for iPhones, Androids and other devices. He began betting on AI way back in 2006.
Nvidia agreed to purchase Arm for up to $40 billion in cash and stock, the companies said Sunday night. This mammoth deal in the chip industry is expected to bolster AI and GPU powerhouse Nvidia's chip portfolio, even as it's sure to attract antitrust attention in the smartphone market. Nvidia will pay Softbank, the company's current owner, a total of $21.5 billion in Nvidia stock and $12 billion in cash, including $2 billion payable at signing. Nvidia will also issue $1.5 billion in equity to Arm employees. It may also pay Softbank up to $5 billion in cash or stock if Arm meets specific financial performance targets--bringing the final purchase price up to $40 billion -- the largest chip deal ever.
Nvidia is close to a deal to buy British chip designer Arm Holdings from SoftBank Group for more than $40 billion (roughly Rs. 2,93,817 crores) in a deal which would create a giant in the chip industry, according to two people familiar with the matter. A cash and stock deal for Arm could be announced as early as next week, the sources said. Nvidia is known for its graphics chips that power video games, but it has developed other markets including artificial intelligence, self-driving cars and data centres. Arm supplies the chip technology for virtually all mobile devices such as phones and tablets but is also expanding into processors for cars, data centre services and other devices. The British company does not make chips.
Just as we expected, NVIDIA just announced that it's buying the semiconductor design company Arm for $40 billion. The deal will make NVIDIA into an even larger presence in mobile computing, especially when it comes to bringing its AI technology into platforms like smartphones, PCs and self-driving cars. Arm, meanwhile, will get even more support for R&D efforts as well as access to NVIDIA's entire suite of products. And to cement its commitment, NVIDIA says it will build an AI supercomputer powered by Arm CPUs at the company's Cambridge headquarters. "AI is the most powerful technology force of our time and has launched a new wave of computing," Jensen Huang, NVIDIA's CEO, said in a statement.
Over the past few decades, software has been the engine of innovation for countless applications. From PCs to mobile phones, well-defined hardware platforms and instruction set architectures (ISA) have enabled many important advancements across vertical markets. The emergence of abundant-data computing is changing the software-hardware balance in a dramatic way. Diverse AI applications in facial recognition, virtual assistance, autonomous vehicles and more are sharing a common feature: They rely on hardware as the core enabler of innovation. Since 2017, the AI hardware market has grown 60-70% annually, and is projected to reach $65 billion by 2025.
Apple is refreshing its 27-inch iMac, though you'll need a keen eye to spot the differences. The new model doesn't look any different from its predecessors, sporting the same classic look Apple has used for several years now with thick bezels surrounding the 5K display. You won't find any radically new features here either. There's still no biometric authentication, meaning there's no Face ID or Touch ID, and the screen uses the exact same panel and pixel resolution as before. Most of the changes are on the inside, and impact performance.