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DUBLIN--(BUSINESS WIRE)--May 24, 2023--
The "Autonomous Driving SoC Research Report, 2023" report has been added to ResearchAndMarkets.com's offering.
Driving-parking integration boosts the industry, and computing in memory (CIM) and chiplet bring technological disruption.
"Autonomous Driving SoC Research Report, 2023" released highlights mainstream automakers' autonomous driving SoC and system deployment strategies, and 9 overseas and 10 Chinese autonomous driving SoC vendors, and discusses the following key issues:
In driving-parking integration market, single-SoC and multi-SoC solutions have their own target customers.
At this stage, Mobileye still rules the roost in the entry level L2 (intelligent front view all-in-one). In the short term, new products like TI TDA4L (5TOPS) pose a challenge to Mobileye in L2. For L2+ driving and driving-parking integration, most automakers currently adopt multi-SoC solutions. Examples include Tesla's `dual FSD`, "triple Horizon J3" on Roewe RX5, `Horizon J3 + TDA4`on Boyue L and Lynk & Co 09, and `dual ORIN` on NIO ET7, IM L7 and Xpeng G9/P7i among others.
According to the production deployment plans of OEMs and Tier 1 suppliers, for lightweight (cost-effective) driving-parking integration, the fusion of driving and parking domains complicates the embedded system design, and poses higher requirements for algorithm model, chip computing power calling (time division multiplexing), computational efficiency of SoC, and costs of SoC and domain control materials.
Cost-effective single-SoC solutions: for passenger cars valued at RMB100,000-200,000, the mass production and deployment of the solutions will peak in 2023. The single-SoC driving-parking integrated solutions generally use Horizon J3/J5, TI TDA4VM/TDA4VH/TDA4VM-Q1 Plus, and Black Sesame A1000/A1000L chips.
High-level driving-parking integration needs access to more cameras with higher resolution, as well as 4D radars and LiDAR. The BEV+Transformer neural network model is larger and more complex, and may even need to support local algorithm training, so it requires high enough computing power, CPU compute up to at least 150KDMIPS, and AI compute up to least 100TOPS.
High-level driving-parking integration targets high-end new energy vehicles priced at not lower than RMB250,000, with low price sensitivity but higher requirements for power consumption and efficiency of AI chips. In particular, high-compute chips have an impact on the endurance range of new energy vehicles, so that chip vendors have to introduce ever more advanced processes and more energy-efficient chip products.
The popularity of ChatGPT indicates the development directions of autonomous driving: foundation models and high computing power. For large neural network models such as Transformer, the computation will multiply by 750 times every two years on average; for video, natural language processing and speech models, the computation will increase by 15 times every two years on average. It is conceivable that Moore's Law will cease to apply, and the `storage wall` and `power consumption wall` will become the key constraints on the development of AI chips.
CIM AI chips will be a new technology path option for automakers.
In the field of autonomous driving SoC, Houmo.ai is the first autonomous driving CIM AI chip vendor in China. In 2022, it successfully lightened the industry's first high-compute CIM AI chip on which the intelligent driving algorithm model runs smoothly. This verification sample uses a 22nm process and boasts computing power of 20TOPS, which can be expanded to 200TOPS. Noticeably the energy efficiency ratio of its computing unit is as high as 20TOPS/W. It is known that Houmo.ai will introduce a production-ready intelligent driving CIM chip soon, and we will share its performance in the report.
In the future, as with power batteries, chips will become an investment hotspot for large OEMs.
That OEMs make chips is an extremely controversial issue. In the industry, it is a popular belief that on one hand, OEMs cannot rival specialist IC design companies in development speed, efficiency, and product performance; on the other hand, only when the shipment of a single chip reaches at least one million units can its development cost can be continuously diluted to make it cost-effective.
But in fact, chips have played an absolutely dominant part in intelligent connected new energy vehicles in performance, cost, and supply chain safety. Compared with the typical fuel-powered vehicle that needs 700-800 chips, a new energy vehicle needs 1,500-2,000 units, and a highly autonomous new energy vehicle even needs as many as 3,000 units, some of which are highly valued, high-cost chips that may be in short supply and even out of stock.
It is obvious that large OEMs do not want to be bound by some chip vendor, and they even have already begun to manufacture chips independently. In Geely's case, the automaker has spawned 7nm cockpit SoCs and installed them in vehicles, and has also accomplished IGBT tape-out. The autonomous driving SoC AD1000, jointly developed by ECARX and SiEngine, is expected to be taped out in March 2024 at the earliest.
We predict that as with power batteries, chips will become an investment hotspot for large OEMs to strengthen their underlying basic capabilities. In 2022, Samsung announced that it will make chips for Waymo, Google's self-driving division; GM Cruise also announced independent development of autonomous driving chips; Volkswagen announced that it will establish a joint venture with Horizon Robotics, a Chinese autonomous driving SoC vendor.
The technical barriers for chip fabrication are not particularly high. The primary threshold is enough capital and order intake. The chip industry now adopts the block-building model, namely, purchasing IPs to build chips including CPU, GPU, NPU, storage, NoC/bus, ISP and video codec. In the future, as chiplet ecosystems and processes get improved, the threshold for independent development of autonomous driving SoCs will be much lower for automakers just need to buy dies (IP chip) directly and then package them, with no need to buy IPs.
In the long run, OEMs with millions of sales are feasible to make chips on their own.
Key Topics Covered:
1 Autonomous Driving SoC Market and Configuration Data
1.1 Autonomous Driving SoC Market Size and Market Share
1.2 Autonomous Driving SoC Deployment Schemes of OEMs
1.3 Application and Configuration Strategy of Autonomous Driving SoC in Driving-parking Integration
1.4 Application Trends of Autonomous Driving SoC in Cockpit-driving Integration
2 Autonomous Driving SoC Selection and Cost
2.1 Comparison of Characteristics between Autonomous Driving SoC Vendors and Their `Turnkey` Solutions
2.2 Autonomous Driving SoC Selection
2.3 Cost of Autonomous Driving SoC
3 Development Trends of Autonomous Driving SoC
3.1 Is It Feasible for OEMs to Independently Make Chips (Autonomous Driving SoC)
3.2 Application of Chiplet in Autonomous Driving SoC
3.3 Application of Computing In Memory (CIM) in Autonomous Driving SoC
4 Global Autonomous Driving Chip Vendors
4.1 NVIDIA
4.2 Mobileye
4.3 Qualcomm
4.4 TI
4.5 Renesas
4.6 Ambarella
4.7 NXP
4.8 Xilinx
4.9 Tesla
5 Chinese Autonomous Driving Chip Vendors
5.1 Horizon Robotics
5.2 Black Sesame Technologies
5.3 SemiDrive
5.4 Huawei
5.5 HOUMO.AI
5.6 Chiplego
5.7 Kunlunxin
5.8 RHINO
5.9 Dahua Leapmotor Lingxin
5.10 Cambricon SingGo
For more information about this report visit https://www.researchandmarkets.com/r/qpnsxl
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SOURCE: Research and Markets
Copyright Business Wire 2023.
PUB: 05/24/2023 07:39 AM/DISC: 05/24/2023 07:39 AM
http://www.businesswire.com/news/home/20230524005506/en