![]() ![]() For example, NVIDIA upgraded its existing GPU-based product line to a three-chip (GPU+CPU+DPU) strategy: In chip design, the configuration of heterogeneous IP is crucial, and autonomous driving SoC chip vendors are constantly strengthening the research and development of core IP to maintain their decisive competitive edges. ![]() Only by forming a developer ecosystem can a company build long-term sustainable competitiveness. At the same time, the development tool chain of SoC chips is very important. Chip bandwidth, peripherals, memory, as well as energy efficiency ratio and cost should be also taken into account. Generally speaking, computing power cannot be simply evaluated from the chip alone. SoC chips, which are mostly involved with heterogeneous design, include different computing units such as GPU, CPU, acceleration core, NPU, DPU, ISP, etc. In addition to computing power, self-developed core IP is the focus of competition for major SoC vendors Although it looks less potent than chips from rivals Qualcomm and NVIDIA, the cost-effective and high-energy-efficiency EyeQ? Ultra? may still be favored by OEMs. As unveiled during CES 2022, EyeQ Ultra maximizes both effectiveness and efficiency at only 176 TOPS, with 5 nanometer process technology. In January 2022, Mobileye introduced the EyeQ? Ultra?, the company’s most advanced, highest performing system-on-chip (SoC) purpose-built for autonomous driving. In this context, ADAS/autonomous driving chips have seen a wave of upgrades, and many chip makers have launched or planned to unveil high computing power chips. The United States is expected to introduce more important policies for autonomous driving in the future to guide 元/L4 autonomous driving on the road. 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. ![]() L2.5 and L2.9 have achieved mass production for vehicles running on the road, and mass production of 元 and L4 in limited scenarios has become a goal for OEMs in the next stage. Autonomous driving chip research: In addition to computing power, core IP, software stacks, AI training platforms, etc. ![]()
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