The global artificial intelligence output value is expected to reach 200 billion U.S. dollars by 2022. If the current trend continues, medical care will occupy a considerable share of this market. In terms of the potential of artificial intelligence technology, this is not surprising. Artificial intelligence can effectively reduce management costs, reduce patient waiting time, and perform self-diagnosis of diseases. Today, Intel and Philips also showed you two other applications of artificial intelligence: bone modeling and lung segmentation.
The Philips Medical Supply and Sensors division published the results of a recent machine learning test. This test was carried out with Intel's Xeon Scalable processor and was also assisted by the OpenVINO computer vision toolkit. The researchers mainly discussed two use cases: the first is to use bone X-rays to simulate changes in bone structure over time, and the second is to use lung CT scans to segment the lungs (that is, to determine the boundary between the lungs and surrounding tissues). ).
They successfully increased the scanning speed of the skeletal age model by 188 times, from the benchmark 1.42 images per second to 267.1 images per second. The speed of the lung segmentation model has been increased by 38 times, from the previous 1.9 images per second optimized to the current 71.7 images per second.
Vijayananda J. Chief Architect, Philips HealthSuite Insights Said: “Intel’s Xeon Scalable processor is clearly tailored for this type of artificial intelligence workload. Our customers can use this processor to maximize their existing hardware, while achieving high speed at extremely high speeds. Quality output resolution."
Intel said that its processor is not just a powerful graphics card used to train and run machine learning models. It has a key advantage in computer vision: it can handle larger and more memory-intensive algorithms.
In May of this year, Intel claimed in a blog post that its Xeon platform outperforms Nvidia's Volta 100 in machine self-inference tasks (such as machine learning translation). And Intel recently announced a Novartis case study, the results of which show that the processing speed of the image analysis model developed by Xeon in early drug discovery is more than 20 times faster than before.
One thing is clear: Intel is preparing for the growth of its artificial intelligence chip business. In August of this year, the company announced that it had sold more than 22 million Xeon processors in the past 20 years, generating $130 billion in revenue. This is a far cry from the expected output value of US$200 billion in the artificial intelligence market in 2022, but the company plans to significantly narrow the gap with expectations by gaining a market share of US$20 billion in the next four years.
Of course Intel is capable of doing this. The chip giant had previously acquired Altera, which allowed field-encoded gate arrays (FPGAs, an integrated, reconfigurable circuit) to join its product lineup. In addition, several other recent acquisitions-namely Movidius and Nervana-have also strengthened Intel's real-time processing business. It is worth mentioning that Nervana's neural network processor is expected to be put into production at the end of 2019, and it is reported that its artificial intelligence training performance is 10 times that of competitive graphics cards.
In addition, Intel said that its upcoming 14-nanometer Cascade Lake architecture processor will be 11 times better than the previous Silver Lake platform in image recognition performance, and the processor will also support a new artificial intelligence The focused instruction set is called DL Boost.
Intel Executive Vice President Navin Sheno said at the data-centric innovation summit held this month: "This is the biggest opportunity for the company in 50 years. We now occupy 20% of the market. Our The main strategy is to promote the arrival of a new era of data center technology."
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