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报告导航:研究报告制造业汽车
2018-2019年ADAS和自动驾驶产业链报告:汽车处理器篇
字数:0.0万 页数:190 图表数:0
中文电子版:12000元 中文纸版:9600元 中文(电子+纸)版:12500元
英文电子版:3000美元 英文纸版:3200美元 英文(电子+纸)版:3300美元
编号:ZYW240 发布日期:2019-04 附件:下载

      汽车智能化的发展,座舱域和智能驾驶域对汽车处理器性能的要求越来越高。

      未来主流的座舱电子功能包括全液晶仪表,三屏是最低配置,五屏甚至六屏都会出现。本地加云端的NLP自然语音处理、手势控制、疲劳检测、人脸识别、AR HUD、高精度地图、V2X都将陆续进入座舱系统。所以座舱对运算资源的需求几乎是无止境,2020年的座舱电子需要50000DMIPS,未来则更多。

汽车处理器行业.png

          自动驾驶对处理器的性能要求则更高,地平线总结了主机厂需求:自动驾驶等级每提高一级,算力就增加一个数量级;L2级别需要2TFLOPS的算力,L3需要24TFLOPS的算力,L4320TOPSL54000+TOPS

 

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    光有算力还不够,考虑汽车应用的复杂性,汽车处理器还需要同时考虑功耗、算力利用率、是否通过车规和安全标准等。

处理器行业2.png

 汽车处理器,又叫汽车计算芯片,包括三种典型产品:ASSP(专用应用标准产品,比如CPUGPU)、ASIC(专用芯片)和FPGA。随着人工智能计算的快速发展,传统的CPUGPU已经开始难以满足越来越多新的需求,在能效上也处于劣势。而半定制的FPGA和定制型的ASIC开始迎来了高速的发展。

 

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 总体上讲,FPGA和ASIC也各有优劣,不同阶段不同领域会使用不同的芯片技术。

 由于智能网联汽车市场对汽车半导体(包括处理器)的巨大需求,吸引了消费电子领域处理器厂商的进入。高通是进入最快的,820E、855E等产品已经在汽车圈获得足够的影响力。全球前25个顶级主机厂品牌中已有18个品牌选用高通处理器。三星、联发科、华为甚至苹果都在追随高通的脚步进入汽车半导体领域。

 除了算力的竞争,工具链也成为处理器厂商竞争的焦点。

 提供更多的配套工具,让用户采用处理器时更方便快捷高效,成为处理器的核心竞争力之一。

英伟达全球副总裁Greg Estes指出:“GPU之上,如果没有软件、没有应用程序,那么没人会买你的GPU”。英伟达的推动下,其开发者社区人数超过100万,有60万个GPU相关的应用程序。

2017年,英伟达发布TensorRT 3 神经网络推理加速器。TensorRT 3能极大改善处理性能,削减从云到边缘设备(自动驾驶汽车、机器人等)的推理开销。TensorRT 3 是在Volta GPU 实现最优推理性能的关键,比起CPU它能实现高达40倍的吞吐量,时延在7ms之内。TensorRT对于英伟达GPU的推广至关重要。

2019CES上,英伟达并没有发布算力更强的处理器,而是扩展其软件工具包,增强落地能力。英伟达整合了此前的Drive Autopilot软件、Drive AGX计算平台与DRIVE Works开发工具,将其打包命名为Drive AP2X。DRIVE AutoPilot负责在地图上准确定位并规划安全高效的行驶路线,Drive Works则为开发人员提供了参考应用程序、工具和一个全面的模块库。

深鉴科技的DNNDK 对标英伟达的 TensorRT。DNNDK提供全自动的压缩与编译工具链等流程的支持,涵盖了神经网络推理(Inference)阶段从模型压缩、异构编程、编译到部署运行的全流程支持,帮助深度学习算法工程师和软件开发工程师实现 AI 计算负载的加速。

因此,2018年7月,赛灵思投入大约3亿美元,完成对成立两年的深鉴科技的收购,助其推广FPGA。

一向封闭的Mobileye,从EyeQ5开始提供SDK工具,SDK的作用除了让车企开发进行神经网络的原型设计和开发,并将其嵌入EyeQ5之外,还能够访问Mobileye预先训练的网络应用层。

2018年7月,英特尔推出了OpenVINO工具包,也主要用于加快高性能计算机视觉和深度学习视觉应用开发。

现在全球有超过70家初创公司做AI芯片,但是真正具备强大工具链开发能力的公司并不多。汽车计算芯片的工具链软件开发上,还需要符合主动安全标准,这是更大的挑战。

国内自动驾驶芯片领头羊地平线提供全栈感知软件和全栈工具链。通过把算法、计算构架以及工具链的协同,地平线使其BPU处理器能够提供比GPU高30倍的性能。

汽车处理器行业4.png
   传统汽车处理器也面临深度学习处理能力欠缺的挑战,正在通过各种方式补上短板。

2019年初,NXP宣布和Kalray合作开发自动驾驶计算平台,双方合作的目的正是弥补NXP的深度学习软肋,Kalray将提供其MPPA处理器的机器学习性能。Kalray的MPPA提供了一个优化的工具和库,允许深度学习或视觉类型算法达到最佳性能。


   瑞萨计划发布的下一代 R-CAR SoC,专为深度学习而生,预计 2020 年开始搭载在 Level 4 自动驾驶汽车上。新款 SoC 将于 2019 年正式推出样品,其计算性能可达 5 万亿次每秒,功耗只有 1 瓦。Renesas 还通过Autonomy联盟,完善其处理器的工具链和生态链。

 


 

As automobiles are going smart, cockpit and intelligent driving require more efficient processors.

Full LCD instrument cluster with at least 3 or even 5 to 6 screens, will be an integral of a mainstream electronic cockpit solution which may be integrated with some local and cloud capabilities such as natural language processing (NLP), gesture control, fatigue detection, face recognition, AR HUD, HD map and V2X. So it can be said that cockpit has endless demand for computational resources, for instance, 50000DMIPS in 2020 and more after the year.

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Autonomous driving needs processors that perform far better. According to Horizon Robotics’ summary of OEM demand, a higher level of automated driving means more orders of magnitude, namely, 2 TOPS for L2 autonomy, 24 TOPS for L3, 320 TOPS for L4 and 4,000+TOPS for L5.

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Source: Horizon Robotics

 Only computing power is not enough. Complexity of automotive applications should be taken into account. That’s because an automotive processor also has to consider how much power is consumed, how much computing power is used or whether it is up to the automotive and safety standards or not.

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Automotive processor, also referred to as automotive computing chip, typically falls into three types: Application specific standard products (ASSP), like CPU and GPU; application specific integrated circuits (ASIC); field programmable gate arrays (FPGA). Conventional CPU and GPU have begun to find it hard to meet increasing new demand as AI computing is developing by leaps and bounds, and in terms of energy efficiency, underperform semi-custom FPGA and full-custom ASIC, both of which are booming.

P4_副本.png
 

By and large, FPGA and FPGA have their own merits and demerits, offering options for different areas

The huge demand from intelligent connected vehicle (ICV) for semiconductors (including processors) is an enticement for the inrush of consumer electronics processor vendors. Take example for Qualcomm, the fastest entrant whose 820E and 855E among other products have won great popularity in automotive sector. Of the top 25 OEMs worldwide, 18 have chosen the giant’s processors. Samsung, MediaTek, Huawei and even Apple follow suit to forge into the automotive semiconductor field.

Processor vendors’ fight is more than in computing power area. Tool chain is also their battleground.

One competitive edge on processor lies in more tools for users’ easier and more efficient use of processors.

“No one will buy your GPU, if you don’t have software and applications”, said Greg Estes, the vice president of NVIDIA, at GTC CHINA 2018. With efforts, the inventor of the GPU has expanded its developer’s community with more than 1 million members and 600,000 GPU applications.

In 2017, NVIDIA unveiled new NVIDIA® TensorRT™ 3 AI inference software that significantly boosts the performance and slashes the cost of inferencing from the cloud to edge devices, including self-driving cars and robots. With TensorRT, the user can get up to 40x faster inference performance comparing Tesla V100 to CPU. TensorRT inference with TensorFlow models running on a Volta GPU is up to 18x faster under a 7ms real-time latency requirement.

At CES 2019, NVIDIA didn’t release more efficient processors but enlarge its software tool kit. The company integrated its previous Drive Autopilot software, Drive AGX computing platform and DRIVE Works development tool into a platform, called Drive AP2X. DRIVE AutoPilot offers precise localization to the world’s HD maps for vehicle positioning on the road and creates a self-driving route. Drive Works provides developers with reference applications, tools and a complete module library.

Deephi Tech’s deep neural network development kit (DNNDK) is an equivalent of NVIDIA TensorRT. DNNDK offers a complete process from neural network inference to model compression, heterogeneous programming, compilation and operation deployment, which is a solution for deep learning algorithm engineers and software development engineers to accelerate AI computing load.

In July 2018, Xilinx acquired Deephi Tech in a USD300 million deal, helping the two-year-old firm promote FPGA.

Starting from EyeQ®5, Mobileye will support an automotive-grade standard operating system and provide a complete software development kit (SDK) to allow customers to differentiate their solutions by deploying their algorithms on EyeQ®5. The SDK may also be used for prototyping and deployment of Neural Networks, and for access to Mobileye pre-trained network layers.

In July 2018, Intel released OpenVINOTM Toolkit for accelerating development of high performance computer vision and deep learning vision application.

There are more than 70 AI start-ups globally, but few of them remain powerful enough to develop tool chain. And conforming to the active safety standards poses a bigger challenge to development of automotive computing chip tool chain.

In China, Horizon Robotics, an autonomous driving chip bellwether, provides full-stack perception software and full-stack tool chain. The way of coordinating algorithms, computing architecture and tool chain enables the firm’s BPU with a performance 30 times higher than GPU.

 P5_副本.png

Automakers are deficient in deep learning capability of their processors as well, and they are going all out to improve weaknesses.

In early 2019, NXP joined forces with Kalray to co-develop an autonomous driving computing platform, with the aim of helping NXP gain muscle in deep learning. The partnership will combine NXP’s scalable portfolio of functional safety products for ADAS and Central Compute with Kalray’s high-performance intelligent MPPA (Massively Parallel Processor Array) processors. MPPA with an optimized tool and a library, enables the best performance of deep learning or vision algorithms.

Renesas plans to roll out next-generation R-CAR SoC for deep learning, which is expected to be mounted on L4 autonomous cars in 2020. The new SoC sample will be unveiled in 2019, and can compute 5 trillion times per second with power consumption of a mere 1W. Also, Renesas upgrades its processor tool chain and ecosystem via its Autonomy Platform.


第一章 汽车处理器(计算芯片)综述
1.1 汽车半导体市场
1.1.1 汽车半导体市场份额
1.1.2 L2-L4级自动驾驶对汽车半导体的单车需求
1.1.3 自动驾驶车辆对不同传感器的需求量预测(L2-L5)
1.2 汽车半导体分类
1.3 汽车计算芯片分类
1.4 GPU
1.5 FPGA
1.6 ASIC
1.7 典型自动驾驶计算芯片对比
1.8 自动驾驶不同环节使用不同的处理器
1.9 典型汽车处理器公司

第二章 座舱处理器及趋势
2.1 座舱电子系统
2.2 座舱处理器概述
2.3 瑞萨座舱处理器
2.4 MBUX及处理器
2.5 INTEL座舱处理器
2.6 高通座舱处理器
2.7 NVIDIA 座舱处理器

第三章 ADAS/AD处理器及趋势
3.1 ADAS和自动驾驶处理器
3.2 3D Bounding Box
3.3 双目和DSP
3.4 英伟达及竞争对手
3.5 ARM A76AE
3.6 MIPS I6500-F
3.7 Xavier
3.8 R-CAR V3H
3.9 自动驾驶处理器的算力要求

第四章 全球汽车处理器厂商研究
4.1 NXP
4.1.1 NXP简介
4.1.2 NXP 处理器和微控制器产品矩阵
4.1.3 NXP i.MX 处理器技术路线
4.1.4 NXP i.MX 处理器应用于座舱
4.1.5 NXP S32系列处理器
4.1.6 NXP自动驾驶计算平台:Bluebox
4.1.7 Bluebox系统架构
4.1.8 NXP与Kalray合作补短板
4.1.9 NXP 自动驾驶发展动向
4.2 Intel/Mobileye
4.2.1 英特尔自动驾驶部门简介
4.2.2 Intel Go
4.2.3 Intel Go用户
4.2.4 Mobileye 的EyeQx产品线
4.2.5 eyeQ芯片的用户及出货量
4.2.6 Mobileye EyeQ5 芯片
4.2.7 EyeQx产品线与INTEL体系整合
4.3 TI
4.3.1 德州仪器简介
4.3.2 德州仪器ADAS 布局
4.3.3 TI 的ADAS芯片:TDAx SoCs
4.3.4 TI ADAS芯片与深度学习
4.3.5 TI TDAx发展路线图
4.3.6 TI DRAx
4.4 英飞凌
4.4.1 Infineon简介
4.4.2 Infineon汽车半导体业务历年收入及增长率
4.4.3 在汽车半导体各细分领域行业地位
4.4.4 Infineon AURIX系列处理器
4.4.5 AURIX和其他自动驾驶计算平台
4.4.6 Infineon对自动驾驶的未来布局
4.5 高通
4.5.1 高通简介
4.5.2 Qualcomm 820A、602A
4.5.3 820A的人工智能
4.5.4 Qualcomm 855A
4.5.5 Qualcomm车载通信系统
4.6 英伟达
4.6.1 英伟达简介
4.6.2 Nvidia DRIVE系列产品参数对比
4.6.3 NVIDIA Parker
4.6.4 Nvidia AGX Xavier
4.6.5 Nvidia AGX Pegasus
4.6.6 Xavier 应用于无人送货
4.6.7 NVIDIA DRIVE AutoPilot
4.6.8 Nvidia DRIVE 系列芯片应用车型示例
4.6.9 Nvidia 自动驾驶合作伙伴
4.7 瑞萨
4.7.1 Renesas瑞萨简介
4.7.2 Renesas MCU & SoC
4.7.3瑞萨的自动驾驶布局
4.7.4 Renesas芯片与竞争对手的对比
4.7.5 Renesas 下一代自动驾驶SoC
4.7.6 Renesas自动驾驶合作伙伴与生态
4.7.7 Renesas autonomy平台
4.7.8 Renesas 芯片在自动驾驶领域的应用案例
4.7.9 Renesas 汽车芯片合作动向
4.8 意法半导体
4.8.1 意法半导体简介
4.8.2 意法半导体ADAS解决方案
4.8.3 意法半导体汽车处理器布局
4.8.4 意法半导体的安全实时微控制器
4.8.5 意法半导体自动驾驶芯片路线图
4.9 ARM
4.9.1 ARM简介
4.9.2 ARM处理器
4.9.3 ARM处理器在汽车上的应用
4.9.3 ARM SoC 在汽车的应用
4.9.4 ARM产品路线图
4.9.5 ARM 自动驾驶技术规划
4.9.6 Cortex-A76AE
4.9.7 Cortex-A65AE
4.9.8 Arm Safety Ready计划
4.9.9 ARM 在自动驾驶领域的动向
4.9.10 ARM的自动驾驶生态
4.10 赛灵思
4.10.1 Xilinx简介
4.10.1 Xilinx的Soc+FPGA系列产品
4.10.2 Xilinx 可拓展产品系列
4.10.3 Xilinx 应用车型及合作伙伴
4.10.4 Xilinx 发力ADAS/自动驾驶市场
4.10.5 Xilinx的 Versal ACAP系列
4.10.6 Xilinx RFSoC发展路线图
4.10.7 Zynq UltraScale+ MPSoC
4.10.8 Xilinx芯片汽车应用案例
4.11 富士通
4.11.1 富士通ADAS方案
4.11.2 富士通代理Miranda
4.12 东芝
4.12.1 东芝简介
4.12.2 东芝 ADAS 解决方案
4.12.3 东芝车载图像识别处理器
4.13 安霸
4.13.1  安霸简介
4.13.2 安霸汽车视觉芯片
4.13.3 安霸与Hella Aglaia合作开发
…………………………………………………………………

第五章 中国汽车处理器厂商研究
5.1 地平线
5.1.1 地平线简介
5.1.2 地平线芯片生态规划
5.1.3 地平线自动驾驶芯片路线图
5.1.4 地平线自动驾驶处理器解决方案
5.1.5 地平线Matrix自动驾驶计算平台
5.1.6 地平线第二代BPU芯片
5.2 杰发科技(四维图新)
5.6.1 杰发科技介绍
5.6.2 杰发科技车载芯片产品线
5.6.3 杰发科技量产车规级MCU芯片
5.3 寒武纪
5.3.1 寒武纪介绍
5.3.2 寒武纪1A和寒武纪1H8
5.3.3 寒武纪自动驾驶芯片
5.3.4 寒武纪商业模式
5.4 森国科
5.4.1 森国科介绍
5.4.2 森国科ADAS芯片
5.4.3 ADAS芯片架构和参数
5.4.4 ADAS芯片算法引擎和支持的算法列表
5.5 全志科技
5.5.1 全志科技介绍
5.5.2 全志科技车规级芯片
5.5.3 全志科技对外合作开发芯片
5.6 华为
5.6.1 华为发布了两款AI芯片,可用于自动驾驶
5.6.2 昇腾310:高效计算低功耗AI SoC
5.6.3 昇腾310应用于自动驾驶
5.6.4 巴龙5000
5.7 联发科
5.7.1 联发科发布汽车芯片品牌
5.7.2 Autus R10
5.8 深鉴科技
…………………………………………………………………

第六章 自研汽车处理器厂商研究
6.1 特斯拉
6.1.1 特斯拉Autopilot系统及处理器演变
6.1.2 特斯拉自研自动驾驶处理器进展
6.2 谷歌
6.2.1 Waymo
6.2.2 Waymo的计算平台架构
6.2.3 谷歌自研TPU芯片
6.3 百度
百度AI芯片“昆仑”
6.4 零跑/大华
零跑携手大华股份联合研发AI自动驾驶芯片
6.5 飞步科技
6.5.1 飞步科技介绍
6.5.2 飞步科技AI芯片核心技术
6.5.3 飞步科技感知芯片
6.6 西井科技
6.6.1 西井科技介绍
6.6.2 西井科技AI芯片核心技术
…………………………………………………………………

1. Overview of Automotive Processors (Computing Chip)
1.1 Automotive Semiconductor Market
1.1.1 Automotive Semiconductor Market Share
1.1.2 Demand of L2-L4 Autonomous Vehicle for Automotive Semiconductors
1.1.3 Demand of Autonomous Vehicle for Different Sensors (L2-L5)
1.2 Classification of Automotive Semiconductors 
1.3 Classification of Automotive Computing Chips
1.4 GPU
1.5 FPGA
1.6 ASIC
1.7 Comparison between Typical Autonomous Driving Computing Chips
1.8 Different Processors Used in Different Links of Autonomous Driving
1.9 Typical Automotive Processor Companies

2. Cockpit Processors and Trends
2.1 Cockpit Electronic System
2.2 Overview of Cockpit Processors
2.3 Renesas Cockpit Processor
2.4 MBUX and Processors
2.5 Intel Cockpit Processor
2.6 Qualcomm Cockpit Processor
2.7 Nvidia Cockpit Processor

3. ADAS/AD Processors and Trends
3.1 ADAS and Autonomous Driving Processors
3.2 3D Bounding Box
3.3 Stereo Camera and DSP
3.4 NVIDIA and Competitors
3.5 ARM A76AE
3.6 MIPS I6500-F
3.7 Xavier
3.8 R-CAR V3H
3.9 Requirements on Computing Power of Autonomous Driving Processors

4. Global Automotive Processor Manufacturers
4.1 NXP
4.1.1 Profile
4.1.2 Processor and Microcontroller Portfolios
4.1.3 i.MX Processor Technology Roadmap
4.1.4 i.MX Processors Applied to Cockpits
4.1.5 S32 Series Processors
4.1.6 Autonomous Driving Computing Platform: Bluebox
4.1.7 Bluebox System Architecture
4.1.8 Collaboration between NXP and Kalray
4.1.9 Autonomous Driving Development Trends
4.2 Intel/Mobileye
4.2.1 Profile
4.2.2 Intel Go
4.2.3 Intel Go Users
4.2.4 Mobileye’s EyeQx Product Line
4.2.5 EyeQ Chip Users and Shipments
4.2.6 Mobileye EyeQ5 Chips
4.2.7 EyeQx Product Line Integrates with the Intel System
4.3 TI
4.3.1 Profile
4.3.2 ADAS Layout
4.3.3 ADAS Chip: TDAx SoCs
4.3.4 ADAS Chip and Deep Learning
4.3.5 TDAx Development Roadmap
4.3.6 DRAx
4.4 Infineon
4.4.1 Profile
4.4.2 Automotive Semiconductor Revenue and Growth Rate
4.4.3 Status in Automotive Semiconductor Segments
4.4.4 Infineon AURIX Series Processors
4.4.5 AURIX and Other Autonomous Driving Computing Platforms
4.4.6 Future Layout in Autonomous Driving
4.5 Qualcomm
4.5.1 Profile
4.5.2 820A and 602A
4.5.3 820A Artificial Intelligence
4.5.4 855A
4.5.5 Automotive Communication System
4.6 Nvidia
4.6.1 Profile
4.6.2 Parameter Comparison between DRIVE Series Products
4.6.3 Parker
4.6.4 AGX Xavier
4.6.5 AGX Pegasus
4.6.6 Xavier for Driverless Delivery
4.6.7 DRIVE AutoPilot
4.6.8 Models with DRIVE Series Chips
4.6.9 Partners in Autonomous Driving
4.7 Renesas
4.7.1 Profile
4.7.2 MCU & SoC
4.7.3 Autonomous Driving Layout
4.7.4 Chip Comparison between Renesas and Its Competitors
4.7.5 Next-generation Autonomous Driving SoC
4.7.6 Autonomous Driving Partners and Ecosystem
4.7.7 Autonomy Platform
4.7.8 Application of Chips in Autonomous Driving
4.7.9 Automotive Chip Cooperation
4.8 STMicroelectronics
4.8.1 Profile
4.8.2 ADAS Solutions
4.8.3 Automotive Processor Layout
4.8.4 Secure Real-Time Microcontrollers
4.8.5 Autonomous Driving Chip Roadmap
4.9 ARM
4.9.1 Profile
4.9.2 Processors
4.9.3 Processors Applied in Automobiles
4.9.3 SoC Applied in Automobiles
4.9.4 Product Roadmap
4.9.5 Autonomous Driving Technology Planning
4.9.6 Cortex-A76AE
4.9.7 Cortex-A65AE
4.9.8 Safety Ready Plan
4.9.9 Dynamics in Autonomous Driving
4.9.10 Autonomous Driving Ecosystem
4.10 Xilinx
4.10.1 Profile
4.10.1 Soc+FPGA Series Products
4.10.2 Scalable Product Series
4.10.3 Models Applied and Partners
4.10.4 ADAS/Autonomous Driving Market
4.10.5 Versal ACAP Series
4.10.6 RFSoC Development Roadmap
4.10.7 Zynq UltraScale+ MPSoC
4.10.8 Chips Applied in Automobiles
4.11 Fujitsu
4.11.1 ADAS Solutions
4.11.2 Agency of Miranda
4.12 Toshiba
4.12.1 Profile
4.12.2 ADAS Solutions
4.12.3 Automotive Image Recognition Processors
4.13 Ambarella
4.13.1 Profile
4.13.2 Automotive Vision Chips
4.13.3 Development with Hella Aglaia

5. Chinese Automotive Processor Companies
5.1 Horizon Robotics
5.1.1 Profile
5.1.2 Chip Ecosystem Planning
5.1.3 Autonomous Driving Chip Roadmap
5.1.4 Autonomous Driving Processors Solutions
5.1.5 Matrix Autonomous Driving Computing Platform
5.1.6 Second-generation BPU Chip
5.2 AutoChips (NavInfo)
5.2.1 Profile
5.2.2 Automotive Chip Product Line
5.2.3 Mass-production of Automotive MCU Chips
5.3 Cambricon
5.3.1 Profile
5.3.2 1A and 1H8
5.3.3 Autonomous Driving Chip
5.3.4 Business Model
5.4 SGKS
5.4.1 Profile
5.4.2 ADAS Chip
5.4.3 ADAS Chip Architecture and Parameters
5.4.4 ADAS Chip Algorithm Engine and Supported Algorithms
5.5 Allwinner Technology
5.5.1 Profile
5.5.2 Automotive Chips
5.5.3 Cooperative Development of Chips
5.6 Huawei
5.6.1 Two AI Chips for Autonomous Driving
5.6.2 Ascend 310: Efficient-computing and Low-power AI SoC
5.6.3 Ascend 310 for Autonomous Driving
5.6.4 Balong 5000
5.7 MediaTek
5.7.1 Automotive Chip Brand
5.7.2 Autus R10
5.8 DeePhi

6. Independent Developers of Automotive Processor
6.1 Tesla
6.1.1 Autopilot System and Processor Evolution
6.1.2 Independent Research Progress in Autonomous Driving Processors
6.2 Google
6.2.1 Waymo
6.2.2 Waymo Computing Platform Architecture
6.2.3 TPUChip
6.3 Baidu
AI Chip "Kunlun"
6.4 Leapmotor / Dahua Technology
Leapmotor Teams up with Dahua Technology to Develop AI Autonomous Driving Chip
6.5 Fabu
6.5.1 Profile
6.5.2 Core AI Chip Technology
6.5.3 Perception Chip
6.6 Westwell
6.6.1 Profile
6.6.2 Core AI Chip Technology

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