Global ADAS/AD Chip Industry Research Report 2022: SoC Vendors Accelerate the Layout of Autonomous Driving AI Data Training
DUBLIN, May 25, 2022 /PRNewswire/ -- The "ADAS/AD Chip Industry Research Report, 2022" report has been added to ResearchAndMarkets.com's offering.
The world's leading autonomous driving AI training chips include: Intel Ponte Vecchio, NVIDIA A100, Tesla D1, Huawei Ascend 910, Google TPU (v1, v2, v3), Cerebras Wafer-Scale Engine, Graphcore IPU, etc.
L2.5 and L2.9 have achieved mass production for vehicles running on the road, and mass production of L3 and L4 in limited scenarios has become a goal for OEMs in the next stage.
In March 2022, the U.S. National Highway Traffic Safety Administration (NHTSA) issued final rules eliminating the need for automated vehicle manufacturers to equip fully autonomous vehicles with manual driving controls to meet crash standards. The United States is expected to introduce more important policies for autonomous driving in the future to guide L3/L4 autonomous driving on the road.
In addition to computing power, self-developed core IP is the focus of competition for major SoC vendors
SoC chips, which are mostly involved with heterogeneous design, include different computing units such as GPU, CPU, acceleration core, NPU, DPU, ISP, etc. Generally speaking, computing power cannot be simply evaluated from the chip alone. Chip bandwidth, peripherals, memory, as well as energy efficiency ratio and cost should be also taken into account. At the same time, the development tool chain of SoC chips is very important. Only by forming a developer ecosystem can a company build long-term sustainable competitiveness.
In terms of domestic vendors, Black Sesame Technologies has launched self-developed NeuralIQ ISP and DynamAI NN engine which is a deep neural network algorithm platform.
SoC vendors accelerate the layout of autonomous driving AI data training
Autonomous driving datasets are critical for training deep learning models and improving algorithm reliability. SoC vendors have launched self-developed AI training chips and supercomputing platforms. Tesla has launched the AI training chip D1 and the "Dojo" supercomputing platform, which will be used for the training of Tesla's autonomous driving neural netwrk.
Besides, training algorithm models are becoming more and more important, including 2D annotation, 3D point cloud annotation, 2D/3D fusion annotation, semantic segmentation, target tracking, etc., such as the NVIDIA Drive Sim autonomous driving simulation platform, the Horizon Robotics "Eddie" data closed loop training platform, etc.
Foreign chip vendors:
Domestic chip vendors:
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