AI calculation force estimation
The development of single CPU can not meet the needs of practical applications, and the AI era must rely on parallel computing. At present, the mainstream architecture of parallel computing is heterogeneous parallel computing platform. If you need the service of computing power, you can go to the tenth power
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on the afternoon of August 23, 2019, Huawei's Shenzhen headquarters released shengteng 910, the most powerful a processor, and launched a full scene AI computing framework Xu Zhijun, chairman of wheel of Huawei, said, Huawei has completed the construction of AI solutions for the whole station and scene, and will launch more AI processors in the future to provide more abundant, more economical and more suitable AI computing power but it gave him a good excuse. He kept shouting that the wolf was coming, the wolf was coming<
The first echelon: Kirin 970 and apple a11: independent AI processing unit and AI software system, as well as open AI underlying technology strength
the second echelon: Qualcomm snapdragon 845 has no independent NPU, and its software system is also independent. The opening of underlying technology depends on various mobile phone manufacturers, unlike Huawei and apple, which have the source of technology
the third echelon: MediaTek P60 lacks an independent NPU, and its software system relies on various companies. Although it has the blessing of oppo R15, it has always been linked with the low-end machines of many manufacturers (Hongmi, Meilan, etc.), so it is not good in momentum or strength
specifically, we can look at the gap in three aspects
Comparison of Architecture:
970 has NPU unit, while 845 is the traditional CPU + GPU + DSP architecture. Snapdragon 845 does not speed up the relevant hardware, but uses DSP to process the relevant operations. In fact, it is meaningless to directly accumulate the computing power of CPU, GPU and DSP. Because these are shared resources, it is impossible to use all of them for AI computing. At present, a network model can not be split into different computing entities. The performance of Kirin 970 only uses NPU, and does not include CPU, GPU and DSP. Compared with the traditional CPU, NPU has 25 times performance and 50 times energy efficiency advantage in the neural network algorithm. At this point, 970 has won a lot
comparison of calculation ability:
through the evaluation of AI running score, we can see that 970 vs. 845 have obvious advantages in basic calculation ability
Master Lu AI score test, Qilin 970 crushing Xiaolong 8451. Master Lu AI score:
Qilin 970 score 233, Xiaolong 845 score 190. 2. NPU is more efficient and full-time: if one word is used to describe Qilin 970's NPU, it is "specialized in technology". Only when it comes to AI task processing, it will be handed over to NPU independent chip for full-time computing processing, Moreover, under the same task, the efficiency of NPU is much higher than that of CPU + GPU + DSP distributed heterogeneous computing
Distributed heterogeneous computing: since CPU, GPU and DSP are not designed specifically for AI computing, it is not surprising that they are not as efficient as single NPU in terms of execution efficiency. The other situation that running score software does not involve: when CPU, GPU and DSP are already in full load, the mobile phone chip can not provide enough AI computing power, and the efficiency will be further greatly recedAI Ecology:
focus on showing the difference between AI and non AI. Qilin 970 hIaI has established an ecology, and new applications appear one after another. At present, Qualcomm is still on paper
specifically, mobile phones can be directly benchmarked, compared with Samsung S9, S9 + app opening, red envelope grabbing, games, etc.
so at present, there are only two companies in the world that master AI chips: apple and Huawei
when it comes to the true flagship of AI, many so-called AI mobile phones in the market are actually embodied in the form of AI experience, such as AI photography and AI voice communication (Samsung S9, Xiaomi, etc. equipped with snapdragon 845), rather than AI based performance. So these are not really AI phones
I think that if we really want to use mobile phones as the flagship of AI, it is the Qilin 970 series procts. Take glory V10 for example. The AI performance of glory V10 comes from the soft and hard AI support of AI chip Kirin 970 and emui8.0 artificial intelligence system. Qilin 970 adopts the hIaI mobile computing architecture. The AI performance of NPU is much better than that of CPU and GPU. It handles the same AI tasks and has about 50 times energy efficiency and 25 times performance advantages. This means that Kirin 970 can complete AI computing tasks faster with less energy consumption, and it also means that CPU and GPU are liberated from AI processing, achieving better results
in short, the three most outstanding and core features of glory V10 are independent AI processing unit, supporting AI operating system, and open underlying AI technology strength and ability. These three points are also the reasons why he can stand out in the same instry. So he's the true flagship of AI
However, I feel that AI chip technology is still in its infancy in mobile phone applications, and the technology has not yet reached the mature stage, and there are still many immature and imperfect places. It takes time for manufacturers to update and promote technology, and it takes time for the market to accept and digest. However, in any case, the era of AI smart phones has come, and the speed is rapid, and the AI chip technology is bound to develop rapidly and matureText: by
lidar, camera and other sensors are just like the eyes of automatic driving vehicles. If a car wants to realize automatic driving, it is essential to have "good eyesight". As a leading sensor supplier in the world, Scala, a proct of Valeo, is the only vehicle size lidar that has been mass proced and installed in the listed models so far. In addition to sensors, there are also a lot of car related AI applications on display at CES and Valeo. Today, I'm in Las Vegas to show you these black technologies that will be displayed at CES
predictive AI movepredict. AI
although the brain's reaction speed is far slower than AI in complex information processing and calculation, for example, human beings are always abused by AI in chess, in fact, the "computational power" of human brain is far higher than AI. The advantage of human brain's high "computational power" can be reflected in some scenes at present, human drivers have much higher ability than automatic driving vehicles, such as the "game" between pedestrians and non motor vehicles when crossing the road, the "game" between themselves and other driving vehicles in the roundabout, and human drivers have better understanding ability and coping style in identifying the driving intention of the target
understanding the heart rate, body shape, body temperature, emotion and other information of each passenger in the vehicle can help the vehicle provide personalized service to the actual needs of each passenger and make the vehicle more intelligent. The heating system of the demonstration car has also been modified. The traditional electric heating (PTC) has been changed into a heat pump which can be controlled by different zones. It can not only meet the temperature requirements of different members in the cockpit, but also improve the utilization efficiency of air conditioning
by using low-energy heat pump, unnecessary heating demand can be avoided (for example, only heating for the driver when only driving), so that the vehicle can save up to 30% of energy consumption in winter. This technology is especially suitable for pure electric vehicles, air conditioning efficiency is higher, also can rece the waste of battery energy consumption, and ultimately effectively improve the pure electric vehicle winter range
this article comes from the author of car home, which does not represent the standpoint of car home
Today, millions of data centers around the world use less than 2 percent of their electricity - a statistic that includes all kinds of workloads being processed on their vast array of servers. Applied materials estimates that servers running AI currently account for only 0.1% of global electricity consumption
Other technology executives have also issued warnings. Anders andrae of Huawei believes that by 2025, data centers may eventually consume one tenth of the world's electricity, but his estimate covers all uses of data centers, not just artificial intelligence
Ironically, the biggest contribution to AI power limitation may actually be AI itself. Google is already using technology developed by deepmind, which it acquired in 2014, to cool its data centers more effectively. By advising human operators, AI has helped the company cut its cooling costs by 40%; Now it can effectively run the cooling system of the data center independently
AI will also be used to optimize other aspects of data center operations. And, just as Google has done with cooling, it will benefit all kinds of workloads. This doesn't mean that the data center will not consume more energy in the end because of the growing demand for AI magic, but this is another reason why it is so difficult to make predictions at present