From the CES and MWC exhibitions in 2017, self-driving cars have become the main battleground of the technology industry because they can sniff out the fronts of various consumer electronics products. Semiconductor companies that have long been eyeing the automotive market have not stopped for a moment. Intel once again took steps to acquire Mobileye, a developer of computer vision chips, for US$15 billion. NVIDIA is not to be outdone, and it will be released together with Bosch. Create an artificial intelligence system. Deconstruction of the traditional vehicle industry chain, increase the proportion of semiconductor plant investment Semiconductor manufacturers have become more and more active, highlighting the development of self-driving cars and car networking, and are deconstructing the industrial chain of the traditional vehicle industry step by step. In 2020, it is a key year for many manufacturers to set a deadline for self-driving. It is necessary to welcome the era of driving. The ADAS (advanced driver assistance system) and the building of a car network constitute the necessary conditions. The mechanized components and system design of passing vehicles have been unable to meet the trend of increasing the control accuracy in the industry and gradually allowing the vehicle components to be electronic. Vehicle-based Telematics products that are not directly related to safety such as navigation and entertainment of vehicles, and vehicle-sensing products that improve safety, such as reversing radar, image recognition systems, and ADAS-related systems, are examples of progressive electronic verification of vehicles. . Therefore, from semiconductors to electronic component manufacturers, the proportion of investment in automotive electronics development has gradually increased. Observing the development of semiconductor manufacturers in the automotive market, manufacturers such as TI, Renesas, and NXP have already worked closely with depots to play a key role in automotive electronics suppliers. In the past, Intel, Qualcomm, and NVIDIA, the semiconductor chip companies focused on PCs and mobile devices, did not plan to make this market pie. In order to occupy the main battlefield of the central processor of the ADAS system, the three companies have been working with depots for nearly a year. Alliances, or even big-ticket mergers and acquisitions, aim at perfecting the technologies used in the future of science and technology and creating "Computer Vision Computer Vision." Technical capabilities have become the unanimous goals of these latecomers. Why is computer vision highly valued? Allowing computers to have visual abilities and capable of interpreting pictures and responding to them is the key to the importance of computer vision technology. In terms of the automotive market alone, with the increasing resolution of the lens and the increasing amount of image data in the ADAS, semiconductor manufacturers must also provide more powerful processors. Extensively speaking, due to processing tasks such as visual signals, whether it is an X86 architecture or an ARM-based Cortex-A series processor, this is not a good project. Better, wasting more CPU core resources, and dragging down the overall processor performance. Therefore, whether the X86 architecture is overlord Intel or ARM through the acquisition of other companies' technology to make up for the lack of the original framework in dealing with computer vision. Intel's acquisition, alliance, and technology development are three ways to target computer vision technology Take a look at the three companies that Intel acquired in the past year. In May last year, Intel’s acquisition of Itseez was the area of ​​technical expertise in computer vision. Itseez developed algorithms that allow vehicles to visually identify obstacles and avoid collisions. Its driver assistance system can detect vehicles appearing on the street or pedestrians crossing the street. In September last year, Intel acquired Movidius. The company’s unique technology lies in mobile image processing technology. Movidius launched the VPU chip, before being acquired by Intel, Movidius to CPU, GPU manufacturers as competitors, its VPU products optimized for computer vision, and provide powerful visual computing capabilities. Intel recently took another shot and acquired Mobileye, which possesses advanced technology advantages such as machine vision, deep learning, data analysis, and high-precision mapping and so on, on the ADAS product, and once again highlights Intel's determination to deploy computer vision technology. Observing Intel's technological capabilities, before the acquisition of Altera, the company's innate CPU architecture made it difficult to enter into applications outside the PC and server market. Until FPGAs became widely available, they opened up more market opportunities. Nowadays, Intel’s mergers and acquisitions launched in recent years are highly related to computer vision. From chip to algorithm are already in place, what Intel will do next is how to properly integrate its product lines. Cut into the car market faster. This kind of highly integrated advantage is rare among many automotive semiconductor manufacturers. Qualcomm and NVIDIA's layout strategies for computer vision Qualcomm demonstrated his determination to enter the automotive market through the acquisition of NXP. Originally there were high-pass CPU, GPU and DSP (digital signal processor) development capabilities, of course, intends to use its DSP technology to enhance computer vision processing, its new generation chip Snapdragon835 equipped with Hexagon68DSP, through the processor's internal heterogeneous architecture Coprocessor capabilities will enable computer vision to work more efficiently and accurately. NVIDIA, on the other hand, makes good use of its own GPU performance and continuously improves its GPU computing capabilities to meet computer vision or other applications that require high-performance computing. A few days ago, NVIDIA also introduced a new processor called Xavier. The 16nm FinFET process is used in this processor. The CPU adopts an eight-core architecture that customizes the ARMv8 instruction set. The GPU is a full-generation Volta with a total of 512 GPUs and an overall computing performance of 20TOPS (trillion operations per second). To sum up, one of the keys to achieving the self-driving vision is to have high-resolution image signals, supplemented by powerful computer vision computing performance. From a chip point of view, the lack of computer vision can only be achieved in the self-driving market. Whether it is Intel, NVIDIA, or Qualcomm, they have taken different approaches to meet this demand, and each one has Different depot partners have launched self-driving cooperation plans, and in the future it will be seen who can take the lead in the market.
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February 01, 2023