Chip computing power algorithm
computing power is a measure of bitcoin network processing power. That is, the speed at which the computer calculates the output of the hash function. Bitcoin networks must perform intensive mathematical and encryption related operations for security purposes. For example, when the network reaches a hash rate of 10th / s, it can perform 10 trillion calculations per second
in the process of getting bitcoin through "mining", we need to find its corresponding solution M. for any 64 bit hash value, there is no fixed algorithm to find its solution M. we can only rely on computer random hash collisions. How many hash collisions can a mining machine do per second is the representative of its "computing power", and the unit is written as hash / s, This is called workload proof mechanism pow
computing power provides a solid foundation for the development of big data, and the explosive growth of big data poses a huge challenge to the existing computing power. With the rapid accumulation of big data in the Internet era and the geometric growth of global data, the existing computing power can no longer meet the demand. According to IDC, 90% of the global information data is generated in recent years. And by 2020, about 40% of the information will be stored by cloud computing service providers, of which 1 / 3 of the data has value
therefore, the development of computing power is imminent, otherwise it will greatly restrict the development and application of artificial intelligence. There is a big gap between China and the advanced level of the world in terms of computing power and algorithm. The core of computing power is the chip. Therefore, it is necessary to increase R & D investment in the field of computing power to narrow or even catch up with the gap with the developed countries in the world
unit of force
1 KH / S = 1000 hashes per second
1 MH / S = 1000000 hashes per second
1 GH / S = 1000000000 hashes per second
1 th / S = 100000000000 hashes per second
1 pH / S = 100000000000 hashes per second
1 eh / S = 100000000000 hashes per second
The types of chips that provide computing power for AI include GPU, FPGA and ASIC
GPU is a kind of microprocessor specialized in image operation on personal computers, workstations, game machines and some mobile devices (such as tablet computers, smart phones, etc.). It is similar to Cu, except that GPU is designed to perform complex mathematical and geometric calculations, which are necessary for graphics rendering
FPGA can complete any digital device function chip, even high-performance CPU can be implemented with FPGA. In 2015, Intel acquired the FPGA long alter head with us $16.1 billion. One of its purposes is to focus on the development of FPGA's special computing power in the field of artificial intelligence in the future
ASIC refers to the integrated circuits designed and manufactured according to the requirements of specific users or the needs of specific electronic systems. Strictly speaking, ASIC is a special chip, which is different from the traditional general chip. It's a chip specially designed for a specific need. The TPU that Google recently exposed for AI deep learning computing is also an ASIC
extended data:
chips are also called integrated circuits. According to different functions, they can be divided into many kinds, including those responsible for power supply voltage output control, audio and video processing, and complex operation processing. The algorithm can only run with the help of chips, and because each chip has different computing power in different scenarios, the processing speed and energy consumption of the algorithm are also different. Today, with the rapid development of the artificial intelligence market, people are looking for chips that can make the deep learning algorithm perform faster and with lower energy consumption
this question is quite professional, but according to my knowledge, I have a chance to finish it let's first introce some basic knowledge about the field of chip manufacturing
I think this kind of problem at most comes from the concern and consideration of China's semiconctor instry manufacturing. Because China's 14nm process has been proced and operated in China, but compared with semiconctor giants like TSMC, we still have a big gap in 5nm and 7Nm. Therefore, there may be such a problem: if we want to use 14nm instead of 5nm, the starting point is very good, but the charm of science lies in constantly exploring the limits and unknowns. Only by constantly climbing can we have a deeper understanding of the world and improve our proctivityWu Wei, director of the mechanical equipment department of the Instrial Development Department of the national development and Reform Commission, said that in the future, automobiles made in China will have the most integration of new technologies and innovations in the world, and will also lead the global automobile instry
at the same time, the competition in the automotive chip field is also extremely fierce. Compared with the chips of consumer electronics procts, automotive chips have higher requirements for safety and stability, which is a common problem faced by the chip instry and an opportunity for Chinese chip companies
conclusion: self developed technology makes Zero run more competitive
Zero run car is the first self-developed auto driving chip among the new Chinese car making enterprises, and the Zero run car C11 with this chip will be released next month. The rapid progress of Zero run vehicles in the field of automatic driving has also been recognized by users
According to the statistical data, the sales volume of the two mass-proced models of Zero run Auto has graally increased since July this year. The sales volume has exceeded 1000 in September, and is expected to exceed 1600 in October. A large number of self-developed technologies have made Zero run a new force of car making more competitivethis article comes from the author of car home, which does not represent the standpoint of car home
on September 18, Huawei released a heavyweight proct, Atlas 900, which brings together Huawei's decades of technological precipitation, is the fastest AI training cluster in the world, and is composed of thousands of ascendant processors. In the resnet-50 model training, the gold standard of AI computing ability, Atlas 900 completed the training in 59.8 seconds, which is 10 seconds faster than the original world record
"imagenet-1k data set" contains 1.28 million images, with an accuracy of 75.9%. Under the same accuracy, the test results of the other two mainstream manufacturers in the instry are 70.2s and 76.8s respectively, and the atlas 900 AI training cluster is 15% faster than the second. Hu houkun said: the powerful computing power of atlas 900 can be widely used in scientific research and business innovation. For example, astronomical exploration, oil exploration and other fields all need to carry out huge data calculation and processing. Originally, it may take several months, but now atlas 900 is just a matter of seconds. The thousands of integrated shengteng processors in atlas 900 are the commercial shengteng 910 some time ago
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The annual salaries of the two scientists reach one million dollars. It is reported that Alibaba artificial intelligence laboratory is mainly composed of R & D personnel, accounting for about 80%. In addition to Chen Ying and Tan Ping, the core proct and technology R & D team also includes Chen Lijuan, the person in charge, Nie Zaiqing, the chief scientist of voice technology, Li Jianye, the chief designer, Ru Yi, the general manager of hardware terminal, Du Haitao, the general manager of proct operation, etc
According to Ali, Dr. Chen Ying's professional accumulation can help Ali improve its visual ability and better empower aiot ecological partners. At the same time, Ali's business can provide rich research scenarios for it to continue to explore the machine's understanding of the world and people. In Tan Ping's opinion, Ali mainly focuses on his own experience in algorithm. "In recent years, AI has been very popular, among which there is more work to do two-dimensional image recognition and detection, but there is still a lack of three-dimensional vision."“ The realistic 3D reconstruction technology that Professor Tan Ping is engaged in can help Ali build 3D models of commodities and stores and create immersive e-commerce experience. " Ali said. Tan Ping said that the team may do a holographic world project in the future. By establishing a three-dimensional digital version of the real world, users can do many things in the real world in the virtual world. For example, users can shop in the virtual store, and the store can also get rid of the space restrictions, and update the space or goods in real time, which is more rich and three-dimensional than the simple commodity list, so as to improve the shopping experience of consumersin addition to shopping in 3D space, Tan Ping and his team will also study the landing of AR or robot navigation procts in the future