Intelligent power calculator
CristianoAmon, President of Qualcomm Corp, at the press conference of SnapdragonRide, shows a combination of SoC, accelerator and automatic driving software stack, showing SnapdragonRide (Graphic source CNET/James Martin)
SnapdragonRide, which provides an extensible solution for automobile manufacturers, which can provide support for self driving vehicles in three sub fields, namely
1, L1 / L2 active safety ADAS -- for vehicles with automatic emergency braking, traffic sign recognition and lane keeping assistance
2, L2 + ADAS -- for vehicles that can drive automatically on expressways, support self-service parking, and drive in urban traffic with frequent parking
3, L4 / L5 fully automatic driving -- for automatic driving, unmanned taxi and robot logistics in urban traffic environment
snapdragonride platform is based on a series of different snapdragon SOC and accelerators. It adopts scalable and molar high-performance heterogeneous multi-core CPU, energy-efficient AI and computer vision engine, and GPU
among them, adassoc series and accelerator series adopt heterogeneous computing. At the same time, using the new generation of artificial intelligence engine of Qualcomm, ADAS and SOC can efficiently manage a large amount of data of on-board system
thanks to the combination of these different SOC and accelerators, snapdragonride platform can be equipped according to the needs of different segments of autonomous driving market, and provide good heat dissipation efficiency, from 30tops level equipment for L1 / L2 level applications to 130W power consumption equipment for L4 / L5 level driving with more than 700tops
in addition, the new snapdragonride autopilot software stack is a molar and scalable solution integrated into the snapdragonride platform
according to the introction, the software framework of snapdragonride platform can host customer specific software stack components and snapdragonride autopilot software stack components at the same time
snapdragonride platform also supports passive or air-cooled cooling design, so it can further optimize vehicle design and improve reliability while recing cost
now, arm, blackberry QNX, Infineon, Xinsi technology, elektrobit, and Anson semiconctor have joined the auto driving circle of friends of Qualcomm and become the software / hardware suppliers of snapdragonride auto driving platform
the functional security solutions of arm, the automotive designware interface IP, arc processor IP and starmemory SystemTM of Xinsi technology, the OS Security version and hypervisor Security version of BlackBerry QNX, the aurixtm microcontroller of Infineon, and the ADAS series sensors of Anson semiconctor will be integrated into the automatic driving platform of Qualcomm
elektrobit also plans to cooperate with Qualcomm to jointly develop a new generation AUTOSAR architecture that can be proced on a large scale. Ebcorbos software and snapdragonride autopilot platform will be integrated on this architecture
it is understood that snapdragonride will be delivered to automobile manufacturers and first tier suppliers for preliminary development in the first half of 2020, and according to the estimation of qcomm technologies, the vehicles equipped with snapdragonride will be put into proction in 2023
Second, we have been deeply engaged in the automotive business for many years. Before the release of snapdragonride, we have been deeply engaged in the field of intelligent vehicles for many yearsfor more than a decade, Qualcomm technologies, a subsidiary of Qualcomm, has been providing advanced wireless communication solutions for GM's connected vehicle applications, including the security applications supported by the Android devices on GM
in the fields of on-board information processing, information audio-visual and in car Internet, the total value of orders of qcomm technologies has exceeded 7 billion US dollars (about 48.7 billion yuan)
according to the information released by Qualcomm at the ces2020 press conference, so far, more than one million vehicles have used the automotive solutions provided by Qualcomm
it is obvious that today, the layout of Qualcomm in the automotive field has taken another step forward
ring ces2020, in addition to snapdragonride, Qualcomm also launched a new car to cloud service, which is expected to be available in the second half of 2020
according to the introction, the car to cloud service built by Qualcomm technologies supports the soft upgrade capability of softsku chip specifications, which can not only help automobile customers meet the changing needs of consumers, but also enable chipsets to be upgraded in the field to support new functions according to new performance requirements or new features
at the same time, softsku also supports customers to develop common hardware, so as to save their special investment for different development projects. By using the cloud softsku of Qualcomm, automobile manufacturers can not only provide a variety of customized services for consumers, but also create rich and immersive in car experience through personalized features
in addition, the car to cloud service of Qualcomm also supports the global cellular connection function, which can be used to guide the initialization service and provide wireless communication connection throughout the life cycle of the car
According to nakul ggal, senior vice president of proct management of Qualcomm technologies, combined with snapdragon's 4G and 5g platforms and snapdragon's digital cockpit platform, Qualcomm's car to cloud service can help automobile manufacturers and first tier suppliers meet the new expectations of contemporary car owners, including flexible and continuous technology upgrading and continuous exploration of new functions throughout the whole automobile life cyclein addition, Qualcomm technologies also announced on ces2020 that it will continue to deepen its cooperation with general motors. As a long-term partner, GM will support digital cockpit, on-board information processing and ADAS (advanced driving assistance system) through continuous cooperation with Qualcomm technologies
conclusion: Giants raging like a storm in the field of automatic driving, HUAWEI has said before it wants to build intelligent core sensors for automobile, such as lidar and millimeter wave radar. After that, Arm led the establishment of the automatic driving Computing Alliance. Now the mobile chip giant Qualcomm has released a new automatic driving platform, and has made further progress in the field of automobile and automatic driving. <
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game player is concive to the faster and better landing of autopilot cars, but on the other hand, as more hardcore players expand their business boundaries, competition in the market will become more intense. p>
this article comes from the author of car home, which does not represent the standpoint of car home
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
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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
in terms of computing power, we know that after we have data, we need to train and train constantly. Because it's not good to train the training set from the beginning to the end. It's just like saying a truth to a child. I'm sure I won't learn it again, except for the prodigy who never forgets. In addition to training, AI actually needs to run on hardware and reasoning, all of which need computing support
so artificial intelligence must have computing power, and with the development of more and more intelligence, more and stronger computing power is needed.
in recent years, artificial intelligence has made everyone feel its very hot and sustainable development. Therefore, we believe that this round of rapid development of artificial intelligence benefits from the rapid development of IT technology over the years, which brings computing power and computing distance to artificial intelligence, so as to provide support for artificial intelligence algorithms
in recent years, the research and development of artificial intelligence technology and various artificial intelligence applications by enterprises have been continuously implemented, which directly promotes the rapid development of the overall artificial intelligence instry. The overall scale of the core instry of artificial intelligence is close to 100 billion yuan, which can be said to be one of the instries with huge scale. Moreover, from the perspective of future development trend, it is estimated that this year, the overall market scale will reach 160 billion yuan, so the growth rate is still very fast
What are the advantages of deep learning
in order to recognize a certain pattern, the usual way is to extract the features of the pattern in a certain way. This feature extraction method is sometimes designed or specified manually, and sometimes summed up by the computer itself under the premise of relatively more data. Deep learning puts forward a method to let the computer automatically learn the pattern features, and integrates the feature learning into the process of modeling, so as to rece the incompleteness caused by artificial design features. At present, some machine learning applications with deep learning as the core have achieved the recognition or classification performance beyond the existing algorithms in the application scenarios that meet specific conditions
if you are interested in artificial intelligence and deep learning, you can take a look at the AI deep learning courses jointly organized by China public ecation and Chinese Academy of Sciences, both of which are taught by experts of Chinese Academy of Sciences in person
in terms of the hardware architecture of the server, AI server is a heterogeneous server. In terms of heterogeneous mode, different combinations can be adopted according to the application scope, such as CPU + GPU, CPU + TPU, CPU + other accelerators, etc. Compared with ordinary servers, there is no difference in memory, storage and network, mainly in big data, cloud computing, artificial intelligence and other aspects, which need more internal and external memory to meet the needs of various data collection and collation
as we all know, the common server is the provider of computing power based on CPU, which adopts the serial architecture and is good at logical computing and floating-point computing. Because a lot of branch jump processing is needed in logic judgment, the structure of CPU is complex, and the improvement of computing power mainly depends on stacking more cores
however, with the application of network technologies such as big data, cloud computing, artificial intelligence and the Internet of things, the amount of data in the Internet is growing exponentially, which poses a serious challenge to the traditional services with CPU as the main source of computing power. At present, the processing technology of CPU and the number of cores of a single CPU are close to the limit, but the increase of data continues, Therefore, the data processing ability of the server must be improved. Therefore, in this environment, AI server came into being.
as we all know, the common server is the provider of computing power based on CPU, which adopts the serial architecture and is good at logical computing and floating-point computing. Because a lot of branch jump processing is needed in logic judgment, the structure of CPU is complex, and the improvement of computing power mainly depends on stacking more cores
however, with the application of network technologies such as big data, cloud computing, artificial intelligence and the Internet of things, the data in the Internet is growing exponentially, which poses a serious challenge to the traditional services with CPU as the main source of computing power. At present, the processing technology of CPU and the number of cores of a single CPU are close to the limit, but the increase of data continues, Therefore, the data processing ability of the server must be improved. Therefore, in this environment, AI server came into being<
nowadays, AI servers in the market generally adopt the form of CPU + GPU, because GPU is different from CPU, which adopts the mode of parallel computing and is good at sorting out intensive data operations, such as graphics rendering, machine learning, etc. On GPU, NVIDIA has obvious advantages. The number of single card cores of GPU can reach nearly 1000. For example, the number of cores with 16 NVIDIA Tesla V100 tensor core 32GB GPUs can exceed 10240, and the computing performance can reach 20 billion times per second. And after years of market development, it has been confirmed that CPU + GPU heterogeneous server in the current environment can really have a lot of development space
but it is undeniable that every instry needs to go through a lot of wind and rain from the beginning to maturity, and in this process of development, competition always exists, and can promote the sustainable development of the instry. AI server can be said to be a trend or a rising force, but there is still a long way to go for AI server. The above is the answer to the tenth power of Inspur server distribution platform.
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<