In the past two years or so, I have visited hundreds of companies and have recently spent time doing targeted and summarizing. In the current field of electronic information, the rhythm of cross-border integration is getting faster and faster, and the convergence of all links in the industry chain is unprecedented. Therefore, looking at a field or a project now requires comprehensive consideration from all aspects of the entire industry chain, including the cloud. The management, including hardware, software, algorithms, and data, and each enterprise in each industry chain, can do forward or backward integration at any time, and the competition and cooperation relationship can be changed at any time. With the advent of the new hardware era, the research on the industry has put forward higher requirements. The latitude of thinking needs to be wider. It is necessary to clarify the intricate relationships and future development trends, and the workload is several times higher than before. The study of benchmarking companies in all aspects of the industry chain is an essential lesson. Only by understanding the strategies and trends of these big companies can we discover the possible entrepreneurial/investment opportunities. So recently, I have consciously done some research on large enterprises, such as the audio, visual, IOT platform, etc., as well as the chip manufacturers mainly targeting ADAS in this article, to understand the main product lines and status of each family.
ADAS (Advanced Driver Assistance System) refers to the use of various sensors installed in the vehicle to collect environmental data in the vehicle at the first time, and to perform technical processing such as identification and detection of static and dynamic objects. , so that the driver can detect the dangers that may occur in the fastest time. Usually includes navigation and real-time traffic system TMC, electronic police system ISA, adaptive cruise ACC, lane offset warning system LDWS, lane keeping system, collision avoidance or pre-crash system, night vision system, adaptive lighting control, pedestrian protection system, Automatic parking system, traffic sign recognition, blind spot detection, driver fatigue detection, downhill control system and electric vehicle alarm system.
At present, most of the innovation of automobiles comes from the innovation of automotive electronics. From the perspective of automotive electronic systems, it is gradually evolved from a decentralized architecture (many ECU control) to a centralized or even central control system (super processor). This trend is the same as the implementation of ADAS. Changes in this trend include reducing ECUs, reducing power consumption, increasing processor and memory utilization efficiency, reducing software development difficulty and improving security, and making automotive semiconductor manufacturers play an increasingly important role in the entire automotive industry. At the same time, for the ADAS processor chip, it now presents a similar product form, such as a smart home, a single product explosion, and a combination of multiple functions, that is, similar to Mobileye's visual processing ADAS chip single product, and multi-sensor fusion, The trend of making ADAS processing chips a platform. At present, these two forms have a market, single function will make ADAS in the middle and low-end car or even after the market, a wider range of popularity, of course, the price is the premise. The fusion of multiple sensors will increase the level of autopilot to Level4 or Level5. At present, unmanned vehicles such as Google and Baidu are doing multi-sensor fusion, but there is no specific Level4/5ADAS ASIC chip.
From the chip design point of view, the main challenges of the ADAS processor chip are as follows:
1) Standards for car gauges, preferably ISO26262, to ASIL-B or even ASIL-D
2) High computational complexity and high bandwidth, especially for multi-sensor fusion chips, require higher chip frequencies, and heterogeneous designs to achieve fast data processing speeds, while also having high throughput requirements.
3) With the application of artificial intelligence in ADAS, the design of the chip will consider increasing the deep learning design of the hardware, how to make trade-offs between hardware and software, and the artificial intelligence computing model and the original software and hardware architecture and the overall system design. Matching is still in the early stages of exploration.
The following mainly introduces the products of various major ADAS processor chip manufacturers, and hopes to get a glimpse of the current status of the ADAS processor chip field and the future development trend from their products.
Qualcomm / NXPSince Qualcomm has acquired NXP, it is introduced here. Qualcomm itself began to gradually enter ADAS through its own mobile processor chip (changed into a car-level). Of course, it has just started to look around. Recently, it has cooperated with the company to launch the first platform based on the Snapdragon 820A at CES. Learning the latest ADAS product prototype, the product runs the 820A Neural Network Processing Engine (SNPE), which can realize multi-class object recognition for vehicles, pedestrians, bicycles, etc., as well as real-time semantic segmentation of pixel-level travelable areas. There is still a certain distance. In general, Qualcomm's product strategy should be based on in-vehicle entertainment information systems, and gradually expand to more professional ADAS.
At the same time, NXP and Freescale, previously acquired by NXP, have a complete product line layout in the automotive electronics and ADAS chips.
NXP has released the Blubox platform to provide OEMs with a solution computing platform for designing, manufacturing and selling Level 4 (SAE) driverless cars. The following figure is a block diagram of NXP's ADAS system, which combines the data of multi-channel video and 77G radar and transmits it to the cloud and body system. We see that NXP is a company that can provide a full set of Reference solutions. The product line in this piece is very comprehensive. Although there is no more chip integration, it provides relatively scattered chips and solutions. Here we focus on the central processor S32V234 and MPC5775K, MPC5775K is to process the radar data, and S32V234 is the fusion analysis of the data processed by the multi-sensor, through the CAN bus, the results are transmitted to the body system.
The S32V234 is an ADAS processor introduced in NXP's S32V series in 2015. It supports heterogeneous computing of CPU (4 ARM V8 architecture A53 and M4), GPU (GC3000) and image recognition processing (CogniVue APEX2 processors), low 5W. Power design. The CogniVue APEX2 processor can simultaneously support four-way car cameras (front, back, left, and right), extract images and classify them, and the GPU can perform real-time 3D modeling with a calculated amount of 50GFLOPs. Therefore, according to this hardware architecture, 360-degree viewing can be completed, and functions such as automatic parking can be completed. At the same time, the chip reserves the interface supporting millimeter wave radar, laser radar and ultrasonic, which is convenient for multi-sensor fusion. The chip supports ISO 26262 ASIL B standard.
The QorivvaMPC567xK series is based on Power Architecture® 32-bit MCUs. The MPC577XK is a dedicated radar information processing chip that adds memory to the chip, improves speed and performance, and supports adaptive cruise control, smart headlight control, and lane departure warning. And blind spot detection applications. From the perspective of the entire radar system, combined with 77G radar transceiver chipset, Qorivva MPC567xK MCU, FPGA, ADC, DAC, SRAM, support long, medium and short range applications. The key areas of interest here are the Signal Processing Toolbox design, which includes FFT, DMA, COPY, and Scheduler. At present, the 77 GHz FCMW radar needs to use FFT in the digital signal processing, that is, fast Fourier transform. The sampling point of the general vehicle radar is about 512-2048. From the chip architecture diagram, we can see the special FFT circuit.
In addition to the S32V series, Freescale has acquired a well-known i.MX series chip that can also be used as a central processing unit. i.MX, especially i.MX6, has a large number of applications on the car, especially on the car information system. Many car manufacturers use i.MX.
Intel/Mobileye/AlteraThrough a series of acquisitions, Intel's layout on the ADAS processor has been improved, including Mobileye's ADAS vision processing, using Altera's FPGA processing, and Intel's own Xeon processor, which can form a self-driving whole hardware super central Controlled solution.
Of particular note is Mobileye's EyeQ series, which has been used by a number of car manufacturers, including Audi, BMW, Fiat, Ford, GM, Honda, Nissan, Peugeot, Citroen, Renault, Volvo and Tesla. The recent EyeQ4 demonstrated performance of 2.5 trillion times per second with operating power as low as 3W. From a hardware architecture perspective, the chip includes a set of industrial-grade quad-core MIPS processors operating at 1 GHz to support innovative multi-threading technology for better data control and management. Multiple dedicated vector microcode processors (VMPs) for handling ADAS-related image processing tasks such as scaling and preprocessing, warping, tracking, lane marking detection, road geometry detection, filtering, and histograms. A military-grade MIPS Warrior CPU is located in the secondary transmission management center and is used to process common data off-chip.
Since the current fusion is mostly radar and camera fusion, the required bandwidth can be met by a general ASIC. But to integrate the laser radar, it is best to use FPGA, FPGA to do sensor Hub is the most suitable. At the same time, sensor fusion is currently less used, and suitable ASICs are not easy to find, and FPGAs have become the mainstream choice. At the same time, for some radars with higher precision requirements, such as using a single-precision floating-point processing to achieve a 4096-point FFT, the more sampling points, the smaller the error, but the amount of computation will increase significantly. It inputs and outputs four complex samples per clock cycle. Each FFT core runs faster than 80 GFLOP, which generally requires an FPGA to perform better. Generally, the sampling point of the vehicle radar is about 512-2048, but the military can reach 8192, and it must be equipped with an FPGA. As shown in the figure below, Altera's CycloneV SoCFPGA is used as the sensor fusion, and is responsible for millimeter wave radar and lidar data processing.
RenesasRenesas offers a complete line of products for the ADAS processor business, as well as the ADAS Kit development system. As far as the chip series is concerned, it is best known for its R-Car product line. The hardware architecture of this series of high-end products includes ARM Cortex A57/53, ARM Cortex R series, Video Codec, 2D/3DGPU, ISP, etc. Support multi-channel visual sensor input, support OPENGL, OpenCV and other software, in line with ASILB vehicle level. In fact, the product was first used in the car infotainment system, and then the system product gradually adapted to the car surround vision system, instrument panel and ADAS system, etc., the development path is worthy of the domestic semiconductor manufacturers who want to enter the automotive field.
In addition to the R-Car series, like NXP, Renesas also has professional processor chips for radar sensors such as RH850/V1R-M series. The product uses 40nm embedded eFlash technology, and the optimized DSP can quickly perform FFT. deal with.
Recently, it has been reported that Renesas has released Renesas Autonomy, a newly designed ADAS and autonomous driving platform. The details are still unclear, but according to Amrit Vivekanand, vice president of automotive business at Renesas Electronics, Renesas's autonomous driving platform is different from its competitors. “This is an open platform, and it is hoped that users will be able to bring them more conveniently. Algorithms, libraries, and real-time operating systems (RTOS) are ported to the platform.†The first product released on the Renesas Autonomy platform is an image recognition system-on-a-chip (SoC) called R-Car V3M. Renesas described the high-performance vision processing chip as "optimized processing unit, preferred for smart camera sensors, and for data processing around surround vision systems and even lidars." Mike, senior analyst from the semiconductor industry analysis firm Linley Group Demler believes that the open platform and products released this time can be seen as a layout of Renesas Electronics' benchmark Mobileye. "They hope to attract automakers who don't cooperate with Mobileye, especially Japanese manufacturers, and hope to attract some. TIer 1 manufacturer of ADAS products. "Renesas is constantly emphasizing the "open" word of the solution compared to the "black box" system of the Mobileye processing platform. This is also the tendency of every processor manufacturer who vows to counter Mobileye. Talking about the problem. Renesas said that all the algorithms of its newly released R-Car V3M processing module will be open to its users.
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