The industry has been looking forward to Intel's machine learning program. What AI solutions will the processor chip leader lead out?
Intel is demonstrating its two new Xeon processors and new FPGA cards for deep learning at the SupercompuTIng 2016 conference in the US; from the company's technology showcase, you can see the complete machine learning solution that it is preparing to launch. One corner.
The industry has been looking forward to Intel's machine learning program, which is one of the hottest technology areas in the semiconductor industry in recent years; the co-founder and CEO of Graphcore, a startup that specializes in artificial intelligence (AI) processor design Nigel Toon said: "I think Brian Krzanich (Intel CEO) will be very big on related technology." Graphcore recently raised $30 million from investors including Intel rival Samsung.
Intel also has some investments in machine learning technology, such as the recent acquisition of two processor innovations Nervana and Movidius, which specialize in neural network technology, plus Altera, which was acquired for $16.7 billion, whose FPGA has been Baidu ( Large companies such as Baidu and Microsoft are used in data centers to speed up search engines and networking.
At the same time, Intel continues to view its large multi-core x86 processor Xeon Phi as a weapon against Nvidia and AMD's graphics processing gas; at the IDF Intel Technology Forum in August, the company said its codenamed Knights Mill's Phi The processor will be the first product to simultaneously play the main processor and accelerator, and is expected to ship in 2017.
Machine learning plays a role that Intel's former CEO Andy Grove might call a "key turning point". The company has a high market share in the server processor market, but machine learning needs to have higher computing performance than those chips. To support highly parallelized tasks.
Google has used its own ASIC to accelerate this reasoning task in machine learning, while Intel's goal is to use a new PCIe card that incorporates Altera Arria FPGAs; Facebook designed its own GPU server using Nvidia's chips to support Train computationally intensive tasks on neural networks.
As for Nvidia, it released its own GPU server earlier this year and worked with IBM on another version of the Power processor; AMD launched an open software project for its GPU earlier this year.
All of the above manufacturers want to compete in the machine learning hardware market - according to market research firm TracTIca, the market size will be 436 million US dollars in 2015, and can further grow to 41.5 billion US dollars by 2024; huge business opportunities Semiconductor startups such as Graphcore, Wave CompuTIng and Cornami have strategically increased their investments, as well as the two companies Intel recently acquired.
Graphcore's Toon said: "Intel's acquisition of Nervana is for software -- I don't think its hardware technology is strong enough;" but an Intel executive said that the company will lock Nervana's hardware to the private cloud enterprise application market. As for Movidius, Toon said its expertise is consistent with Intel's RealSense family of 3D cameras and is suitable for low-power system applications at the network/IEF edge nodes.
“To be honest, I am not sure what role Altera will play;†Toon joined Altera in 1988 and is responsible for the promotion of its European operations: “Intel may somehow try to use FPGA as a machine learning accelerator, but I don’t think That's the right approach.†He pointed out that Graphcore's multi-core chips will be a higher performance solution than FPGAs or GPUs in deep learning applications.
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