임바디드 AI(휴머노이드 로봇)용 메인 제어 SoC 조사 보고서(2026년)

Embodied AI SoC Research: Chip Vendors Are Transforming from "Single SoC Vendors" to "Full-Stack Chip Platform Providers".

The advancing chip technology provides a crucial boost to the booming embodied artificial intelligence (EAI) industry. Robots for different application scenarios have differentiated chip selection requirements, avoiding problems caused by improper selection, such as excessive computing power surplus or insufficient performance. In addition, the development of the EAI industry also relies on breakthroughs in large model technology. The intelligence level of robots has been significantly improved, enabling robots to make independent judgments and perform complex tasks.

As the EAI Market Continues to Expand and Chip Performance Requirements Keeps Rising, Chip Vendors Launch Full-Stack Solutions.

The robot chip market is in a period of rapid growth. The global shipments of general-purpose EAI robots reached 13,000 units in 2025 and is expected to exceed 50,000 units in 2026. At present, major chip giants have launched SoCs for EAI, such as NVIDIA Jetson series and Qualcomm IQ10 series. Meanwhile, they provide robot development platforms, including NVIDIA Isaac open-source platform, second-generation Rockchip RKNN neural network model conversion and optimization tool, and Black Sesame SmartX multi-dimensional intelligent computing platform for the robot industry, in a bid to meet customers' needs for rapid application deployment and model development.

Currently, EAI SoCs are evolving:

Trend 1: Requirements for Chip Computing Power Become Much Higher.

NVIDIA's new Jetson T5000 adopts the Blackwell GPU architecture, delivering up to 2070 FP4 TFLOPS of AI compute, 7.5x higher than the previous-generation Jetson Orin. The RDK S100P from Horizon Robotics (D-Robotics) integrates CPU+BPU+MCU on a single chip, delivering 120 TOPS computing power. With the increasing complexity of algorithms, robots' computing power demand is gradually rising from the current 200-500 TOPS to 500-1000 TOPS. Notably, the industry no longer simply stacks computing power but shifts to "efficiency priority". Algorithm optimization makes efficiency a core indicator.

Trend 2: Chip Vendors Evolve towards Advanced Processes

Mainstream chip vendors move towards advanced processes. NVIDIA Jetson AGX Thor adopts 4nm process, Intel Core Ultra Series 3 uses Intel 18A process, Rockchip RK3588 adopts 8nm process, and MediaTek's latest Genio Pro adopts 3nm process, substantially boosting chip performance.

Trend 3: EAI Chip Vendors Are Transforming from "Single SoC Vendors" To "Full-Stack Chip Platform Providers".

In SemiDrive's case, besides EAI cerebrum SoCs, it has also launched intelligent control cerebellum SoCs and high-performance MCUs, so as to build full-stack EAI solutions, covering the complete architecture of "cerebrum - cerebellum - body- joint". Its product matrix ranges from main control cerebrum SoCs for high-level cognition and decision, and intelligent control cerebellum chips for motion coordination and real-time control, to E3-R series MCUs for LiDAR/machine vision, motion center, dexterous hands and joint modules, realizing full-chain chip coverage.

Among them, the intelligent control cerebellum D9-Max and robot joint module MCU E311x-R have come into mass production, and built in-depth cooperation with leading robot enterprises, successfully bringing automotive-grade high performance and high reliability into the robot field.

D9 Max adopts an architecture optimized for cerebellum application. Based on hardware isolation and hardware virtualization technology, it integrates one 8-core 2.0GHz Cortex-A55 CPU cluster, one 4-core 2.0GHz Cortex-A55 CPU cluster, and 3 pairs of dual-core lockstep 800MHz Cortex-R5F, as well as computing units like 8TOPS NPU and GPU. A single chip allows for deployment of three core functions of motion control system, HMI and EtherCAT master station, integrating the functions that traditional solutions require three chips to enable into a single chip.

The high-performance MCUs (E3-R series) have made substantial progress in joint control, meeting high functional safety and cybersecurity requirements and providing one-stop solutions. As the main control chip for joint modules, E311x-R features high real-time performance and highly stable computing power output capability. It adopts dual R5F cores with a main frequency up to 400MHz. In actual R&D, the dual cores separate motor control and communication processing for dedicated core allocation and enhanced performance.

In terms of EAI cerebrum SoC, SemiDrive reuses its expertise in on-device large model capabilities in the automotive sector to develop the next-generation robot cerebrum chip R1. Adopting ARM V9.2 architecture CPU and new high-performance NPU, it supports on-device deployment of embodied end-to-end models such as MLLM/VLA under low power consumption.

Trend 4: Chip Vendors Are Launching Full-stack Self-developed Toolchains.

Rockchip launched RKNN-Toolkit2, its second-generation neural network model conversion and optimization tool. Acting as a bridge connecting mainstream deep learning frameworks and Rockchip NPU (Neural Processing Unit) hardware platforms, it is designed to help developers efficiently deploy trained AI models on embedded devices. Based on Huashan A2000, Black Sesame Technologies builds the easy-to-use Shanhai AI toolchain, covering the entire process from model optimization to on-device deployment, providing developers with an efficient model development and deployment system. SemiDrive offers complete software and hardware development kits such as the D9-Max application development kit, enabling customers and independent developers to rapidly deploy applications and conduct on-device development.

Selection of Chips and Algorithms by Embodied Robot OEMs

The EAI level is essentially the result of the co-evolution of algorithms and chips. The two are interdependent and mutually driven, forming the core closed loop of robot intelligent systems.

For example, the basic computing board of AgiBot Lingxi X2 adopts two Rockchip RK3588 chips, replacing Jetson Xavier adopted by the previous-generation, offering improvements in both cost and performance. The 6TOPS NPU of RK3588 delivers excellent performance in motion control and perception fusion scenarios while reducing power consumption by 7W. The high-compute board adopts NVIDIA Jetson Orin NX, with total AI compute of 169 TOPS.

In terms of algorithms, the cerebrum of Lingxi X2 is equipped with AgiBot's self-developed large model Genie Operator-1 (GO-1). Adopting the Vision-Language-Latent-Action (ViLLA) architecture composed of VLM (multimodal large model) and mixture-of-experts (MoE), Lingxi X2 possesses superior learning, fast few-shot generalization and continuous evolution capabilities. The cerebellum of Lingxi X2 adopts the Xyber-Edge controller for robot motion coordination and decision. With a 144-core heterogeneous computing architecture, the controller dynamically allocates reasoning tasks to NPU clusters, and control commands to FPGAs, and compresses the traditional 12-layer control architecture for motion planning into a 3-layer implicit planning structure, achieving 450Hz real-time closed-loop control, greatly superior to Tesla Optimus' 280Hz closed-loop frequency.

AgiBot has made a differentiated and complementary layout by launching three product series of Yuanzheng, Lingxi and Genie, targeting industrial manufacturing, commercial services and data research scenarios, respectively, and is advancing towards mass production and commercial deployment.

Table of Contents

1 EAI Market and Application Scenarios

• 1.1 Basic Concepts and Terminology of EAI
• Basic Concepts of EAI (1)
• Basic Concepts of EAI (2)
• Basic Concepts of EAI (3)
• Terminology of EAI (1)
• Terminology of EAI (2)
• 1.2 Market Prospect of EAI
• Evolution History of EAI
• Status Quo of EAI Industry
• Evolution of EAI Application Scenarios (1)
• Evolution of EAI Application Scenarios (2)
• EAI Market Trends
• China EAI Market Size
• Global Humanoid Robot Shipments
• 1.3 Application Prospect of EAI
• Summary of Application Prospects
• EAI Market Structure by Application Scenario
• Community & Household Scenario: Household Service (1)
• Community & Household Scenario: Household Service (2)
• Community & Household Scenario: Medical / Nursing Scenario (1)
• Community & Household Scenario: Medical / Nursing Scenario (2)
• Smart Manufacturing Scenario: Factory Production
• Smart Manufacturing Scenario: Figure Humanoid Robots Realize 24/7 Operation in Factories
• Smart Manufacturing Scenario: UBTECH Walker S2 Group Collaborative Operation in Smart Factories
• Smart Manufacturing Scenario: Agricultural Production
• Commercial Service Scenario: KEENON Robotics
• Commercial Service Scenario: Meituan "Little Wasp"
• High-Risk & Rescue Scenario: DEEP Robotics LYNX M20 Wheeled-Legged Robot
• High-Risk & Rescue Scenario: iFreecomm "Lingmu" Emergency Rescue Quadruped Robot and Guide Dog
• 1.4 Competition Summary of EAI Suppliers
• Top 50 Chinese EAI Suppliers (1)
• Top 50 Chinese EAI Suppliers (2)
• Top 10 Foreign EAI Suppliers (1)
• Global Shipments of Top 10 Humanoid Robots, 2025 (Mainstream Statistical Caliber)
• Revenues of Representative EAI Enterprises (1)
• Revenues of Representative EAI Enterprises (2)
• Technical Routes of Representative EAI Enterprises (1)
• Technical Routes of Representative EAI Enterprises (2)
• Technical Routes of Representative EAI Enterprises (3)

2 Software and Hardware System Architecture of EAI

• 2.1 Hardware Architecture of EAI
• EAI: Introduction to Hardware System
• EAI Hardware List
• EAI Chip List
• SemiDrive: Full-Stack Chip Solutions for Robots
• GigaDevice: Full-Stack Chip Solutions for Robots
• Infineon: Solutions for Each Functional Module of Humanoid Robots (1)
• Infineon: Solutions for Each Functional Module of Humanoid Robots (2)
• Infineon: Product Layout for Humanoid Robots
• 2.1.1 EAI Hardware System: Computing Power and Hardware Control System
• EAI Hardware System: Computing Power and Hardware Control System
• EAI Hardware System: Composition of the "Cerebrum" System
• EAI Hardware System: "Cerebrum" System - Application of Main Control SoC
• EAI Hardware System: "Cerebellum" System - Application of FPGA
• EAI Hardware System: "Cerebellum" System - Application of MCU
• EAI Algorithm: Cerebrum Control Technical Route - Vision-Language-Action (VLA) Model
• EAI Algorithm: Cerebrum Control Technical Route - Hierarchical Planning Architecture
• EAI Algorithm: Cerebrum Control Technical Route - Cross-Robot General System
• EAI Algorithm: Cerebellum Control Technical Route - Model-Based Control Method
• EAI Algorithm: Cerebellum Control Technical Route - Imitation Learning
• EAI Algorithm: Cerebellum Control Technical Route - Deep Reinforcement Learning
• EAI Algorithm: Cerebrum-Cerebellum Collaboration Mechanism - Traditional Hierarchical Collaboration Architecture
• EAI Algorithm: Cerebrum-Cerebellum Collaboration Mechanism - New Brain-Inspired Three-System Architecture ("Cerebrum - Pons - Cerebellum")
• 2.1.2 EAI Hardware System: Mechanical System
• EAI Hardware System: Mechanical System (Bionic Skeleton)
• EAI Mechanical System: Joint Module
• EAI Mechanical System: Joint Module - Motor and IC
• EAI Mechanical System: Joint Module - Reducer
• EAI Mechanical System: Joint Module - Driver and Encoder
• 2.1.3 EAI Hardware System: Execution System
• EAI Hardware System: Execution System (Bionic Muscle)
• 2.1.4 EAI Hardware System: Power Supply and Thermal Management System
• EAI Hardware System: Power Supply System
• EAI Hardware System: Thermal Management System
• 2.1.5 EAI Hardware System: Perception System
• EAI Hardware System: Perception System
• EAI Hardware System: Perception System Framework
• EAI Hardware System: Perception System - Vision Sensor Technology
• EAI Hardware System: Perception System - Radar Sensor Technology
• EAI Hardware System: Perception System - Inertial Measurement Unit (IMU) Technology
• 2.2 Software Architecture of EAI
• Introduction to EAI Software Architecture
• EAI Software Architecture: Hardware Abstraction Layer (HAL)
• EAI Software Architecture: Driver Execution Layer
• EAI Software Architecture: Real-Time Control Layer
• EAI Software Architecture: Decision & Planning Layer
• EAI Software Architecture: Application Layer (Non-Real-Time Layer)
• 2.3 Communication Architecture of EAI
• Communication Protocol of EAI
• Communication Protocol of EAI: Hierarchical Architecture
• Communication Protocol of EAI: Working Mechanism of EtherCAT
• Communication Protocol of EAI: Structure of EtherCAT
• Communication Protocol of EAI: Working Mechanism of CAN
• Communication Protocol of EAI: Working Mechanism of CAN FD
• Communication Protocol of EAI: CAN FD Network Framework
• 2.4 Grading Standard for EAI
• Levels of EAI
• Current Technical Level of EAI (1)
• Current Technical Level of EAI (2)
• Current Technical Level of EAI (3)

3 EAI Cerebrum (Main Control SoC, Controller and Large Model)

• 3.1 EAI Main Control SoC: Summary of Robots and Grouped Chips
  • 3.1.1 EAI Main Control SoC: Summary of Robots and Grouped Chips -Humanoid Robots
  • Mainstream On-device Chips and Algorithms for Humanoid Robots
  • Humanoid Robots: Ubtech Walker S2, AgiBot Lingxi X2
  • Humanoid Robots: Unitree H2, Leju KUAVO 5
  • Humanoid Robots: Booster K1, Noetix Bumi
  • Humanoid Robots: EngineAI T800, ROBOTERA L7
  • Humanoid Robots: Fourier Intelligence GR-3, Xpeng IRON
  • Humanoid Robots: Xiaomi CyberOne, Figure AI Figure 03
  • Humanoid Robots: Tesla Optimus Gen 3
  • Humanoid Robots: Noetix Hobbs 3 (Xiaonuo)
  • 3.1.2 EAI Main Control SoC: Summary of Robots and Grouped Chips -Quadruped Robots
  • Mainstream On-device Chips and Algorithms for Quadruped Robots
  • Quadruped Robots: Unitree As2, Xiaomi CyberDog
  • 3.1.3 EAI Main Control SoC: Summary of Robots and Grouped Chips - Other Robots
  • Mainstream On-device Chips and Algorithms for Other Types of Robots
  • Dual-Arm Mobile Robot: GigaAI Maker H01
• 3.2 EAI Main Control SoC: Summary of Chip Vendors
• Revenues of EAI Chip Vendors
• EAI Chip Vendors: Product List of SemiDrive
• EAI Chip Vendors: Core Products and Evolution Route of SemiDrive
• EAI Chip Vendors: Product List of NVIDIA
• EAI Chip Vendors: Core Products and Evolution Route of NVIDIA
• EAI Chip Vendors: Product List of Qualcomm
• EAI Chip Vendors: Core Products and Evolution Route of Qualcomm
• EAI Chip Vendors: Product List of Intel
• EAI Chip Vendors: Core Products and Evolution Route of Intel
• EAI Chip Vendors: Product List of MediaTek
• EAI Chip Vendors: Core Products and Evolution Route of MediaTek
• EAI Chip Vendors: Product List of Rockchip
• EAI Chip Vendors: Core Products and Evolution Route of Rockchip
• EAI Chip Vendors: Product List of Black Sesame Technologies
• EAI Chip Vendors: Core Products and Evolution Route of Black Sesame Technologies
• EAI Chip Vendors: Product List of Cambricon
• EAI Chip Vendors: Core Products and Evolution Route of Cambricon
• 3.3 Technical Evolution Route of EAI Main Control SoC
• Trend 1:
• Trend 2:
• Trend 3:
• 3.4 EAI Controller: Summary of Suppliers
• EAI Controller: Revenues of EAI Controller Suppliers
• EAI Controller: Product List of SEER Robotics
• EAI Controller: Core Products and Evolution Route of SEER Robotics
• EAI Controller: IMotion
• EAI Controller: Luxshare Precision
• EAI Controller: SIM Technology
• EAI Controller: Chengdu Ruixingxing
• EAI Controller: NIIC
• EAI Controller: Pegasus?
• EAI Controller: Inovance Technology
• EAI Controller: Huacheng Industrial Control
• 3.5 Summary of EAI Large Models
  • 3.5.1 EAI Large Model: VLA
  • Vision-Language-Action (VLA) Model
  • Origin of VLA Model: RT-1 and RT-2
  • Technical Deepening of VLA Model: OpenVLA
  • Wide Application of VLA Model: Figure AI Helix Model
  • Wide Application of VLA Model: NVIDIA GR00T N1
  • Wide Application of VLA Model: ByteDance GR-3 Model
  • Wide Application of VLA Model: Horizon Robotics Released Full-Stack Open-Source VLA Foundation Model HoloBrain-0
  • 3.5.2 EAI Large Model: World Model
  • Basic Architecture of World Model
  • Key Definition and Application Development of World Model
  • Summary of EAI World Models
  • AgiBot and Shanghai AI Lab Jointly Proposed Embodied 4D World Model EnerVerse
  • 3D-VLA: A 3D Vision-Language-Action Generative World Model
  • RoboDreamer: Learning Compositional World Models for Robot Imagination
  • IRASim - World Model in Robotics
  • Amap: ABot General EAI System (1)
  • Amap: ABot General EAI System (2)
  • UnifoLM-WMA: Unitree Open-Source World Model
  • 3.5.3 Lightweight Deployment of EAI Models
  • Technical Requirements for Lightweight Model Deployment
  • Combination of Multimodal Fusion and Lightweight Technology
  • Lightweight Technology: Cross-Modal Feature Compression
  • Lightweight Technology: Dynamic Modal Selection
  • Lightweight Technology Implementation: HugWBC General Humanoid Robot Controller
  • Lightweight Technology Implementation: HOVER Multimodal Neural Network Controller
  • Lightweight Technology Implementation: AMS (Agility Meets Stability) Framework

4 Mainstream EAI Robot Integrators

• 4.1 UBTECH
• Products and Operation
• Product Strategy
• Overview of Robot SoC Configurations
• Overview of Robot Model Algorithms
• Parameter Comparison between General Humanoid Robots (1)
• Parameter Comparison between General Humanoid Robots (2)
• Parameter Comparison between General Humanoid Robots (3)
• Humanoid Robot Walker S2: Dedicated Agent Technology
• Humanoid Robot Walker S2: EAI Large Model Thinker
• Humanoid Robot Walker S2: Self-Service Battery Swap System
• Humanoid Robot Walker S2: End-to-End Human-Like Stereo Vision Perception
• 4.2 AgiBot
• Profile
• Overview of Robot SoC Configurations (1)
• Overview of Robot SoC Configurations (2)
• Overview of Model Algorithms
• Parameter Comparison between Humanoid Robots (1)
• Parameter Comparison between Humanoid Robots (2)
• Parameter Comparison between Humanoid Robots (3)
• Humanoid Robot: Embodied Foundation Model Genie Operator-1
• Humanoid Robot: Self-Developed Controller System
• Humanoid Robot: Million-Level Real Robot Dataset Open-Source Project AgiBot World
• Humanoid Robot: Powerflow Core Joint Module and WITA Interactive Large Model
• Supply Chain (1)
• Supply Chain (2)
• 4.3 Unitree Robotics
• Profile
• Overview of Robot SoC Configurations (1)
• Overview of Robot SoC Configurations (2)
• Overview of Model Algorithms
• Parameter Comparison between Quadruped Robots (1)
• Parameter Comparison between Quadruped Robots (2)
• Parameter Comparison between Quadruped Robots (3)
• Parameter Comparison between Quadruped Robots (4)
• Parameter Comparison between General Humanoid Robots (1)
• Parameter Comparison between General Humanoid Robots (2)
• Parameter Comparison between General Humanoid Robots (3)
• Consumer-grade Quadruped Robot As2: Bionic Embodied Large Model
• Consumer-grade Quadruped Robot As2: Self-Developed 4D LiDAR L2
• Supply Chain
• Customer Base
• 4.4 Leju Robotics
• Profile
• Product Overview
• Overview of Robot SoC Configurations
• Overview of Model Algorithms
• Parameter Comparison between Robot Products (1)
• Parameter Comparison between Robot Products (2)
• Parameter Comparison between Robot Products (3)
• Full-Stack Data Collection and Model Training System
• Leju Research Framework 2.0 (1)
• Leju Research Framework 2.0 (2)
• Partners
• 4.5 Booster Robotics
• Profile
• Overview of Robot SoC Configurations
• Parameter Comparison between Robot Products (1)
• Parameter Comparison between Robot Products (2)
• 4.6 Noetix Robotics
• Profile
• Overview of Robot SoC Configurations
• Overview of Model Algorithms
• Parameter Comparison between General Humanoid Robots (1)
• Parameter Comparison between General Humanoid Robots (2)
• Parameter Comparison between Bionic Humanoid Robots (1)
• Parameter Comparison between Bionic Robot Products (2)
• Self-Developed "Lingjiu" Motion Control Algorithm
• Bionic Robot: Self-Developed Second-Generation Bionic Head Platform
• Self-Developed Expression Driven Algorithm and Multimodal Interaction Large Model
• 4.7 EngineAI Robotics
• Profile
• Overview of Robot SoC Configurations of
• Parameter Comparison between Robot Products (1)
• Parameter Comparison between Robot Products (2)
• Motion Control Algorithm Patent: Sim2Real Technology
• Energy and Structural Patents
• Joint Technology Patents
• Supply Chain
• 4.8 ROBOTERA
• Profile
• Overview of Robot SoC Configurations
• Overview of Model Algorithms
• Parameter Comparison between Robot Products (1)
• Parameter Comparison between Robot Products (2)
• Ctrl-World World Model
• VLAW Framework
• Self-Developed Native End-to-End Embodied Large Model ERA-42
• ROBOTERA XHAND1 Dexterous Hand
• Supply Chain and Cost Composition: Self-Developed Core Components + Cooperation with Strategic Suppliers
• 4.9 Fourier Intelligence
• Profile
• Overview of Robot SoC Configurations
• Parameter Comparison between General Humanoid Robots (1)
• Parameter Comparison between General Humanoid Robots (2)
• Parameter Comparison between General Humanoid Robots (3)
• FSA 2.0 Actuator
• Galileo System
• 4.10 GigaAI
• Profile
• Product Parameters
• GigaBrain
• GigaWorld
• 4.11 Xpeng IRON
• Profile
• IRON Robot: Commercialization Progress and Future Planning
• IRON Humanoid Robot: Product Parameter Comparison (1)
• IRON Humanoid Robot: Product Parameter Comparison (2)
• IRON Humanoid Robot: Product Parameter Comparison (3)
• IRON Humanoid Robot: Product Parameter Comparison (4)
• IRON Humanoid Robot: Product Parameter Comparison (5)
• IRON Robot Main Control SoC: Self-Developed Turing AI Chip
• IRON Robot Main Control SoC: Detailed Parameters of Self-Developed Turing AI Chip
• IRON Robot Main Control SoC: Parameter Interpretation of Self-Developed Turing AI Chip
• IRON Robot AI Large Model: Application of Second-Generation VLA Physical World Large Model
• IRON Robot Cloud Foundation Model: Reusable with Automobiles
• IRON Robot Perception System: Hawk-Eye Vision System
• IRON Robot Cost and Supply Chain Composition: Cost of the First-Generation IRON
• 4.12 Xiaomi
• Parameters of CyberOne Robot (1)
• Parameters of CyberOne Robot (2)
• Parameters of CyberOne Robot (3)
• Parameters of CyberDog Quadruped Robot
• Robot: VLA Foundation Model Xiaomi-Robotics-0 (1)
• Robot: VLA Foundation Model Xiaomi-Robotics-0 (2)
• Robot: Self-Developed Software Algorithm
• Robot: CyberOne Bionic Hand (1)
• Robot: CyberOne Bionic Hand (2)
• Robot: Self-Developed Power System
• Robot: Cost and Supply Chain Composition
• Robot: Commercialization Progress and Future Planning
• 4.13 Tesla
• Parameters of Tesla Optimus (1)
• Parameters of Tesla Optimus (2)
• Parameters of Tesla Optimus (3)
• Mainstream On-device Computing Chip for Humanoid Robots: Tesla A15
• Tesla Optimus Gen 3 Motion Control: Reinforcement Learning Model Trained by Dojo Supercomputer
• Tesla Optimus Gen 3: Reuse FSD V12/V13 Vision-only Neural Network Architecture (1)
• Tesla Optimus Gen 3: Reuse FSD V12/V13 Vision-only Neural Network Architecture (2)
• Tesla Optimus Gen 3: Reuse FSD V12/V13 Vision-only Neural Network Architecture (3)
• Tesla Optimus Gen 3: Reuse FSD V12/V13 Vision-only Neural Network Architecture (4)
• Tesla Optimus Gen 3: Reuse FSD V12/V13 Vision-only Neural Network Architecture (5)
• Tesla Optimus Gen 3: Motion Planning Algorithm
• Tesla Optimus Gen 3: Dexterous Hand (1)
• Tesla Optimus Gen 3: Dexterous Hand (2)
• Tesla Optimus Gen 3: Dexterous Hand (3)
• Supply Chain of Tesla Optimus
• 4.14 Figure AI
• Profile
• Overview of Robot SoC Configurations and Model Algorithms
• Parameter Comparison between General Humanoid Robots
• Robot: Helix AI Model
• Robot: BotQ Humanoid Robot Factory
• Supply Chain

5 Mainstream EAI Chip Vendors

• 5.1 SemiDrive
• Application and Planning of EAI Products
• Strategy 2.0 from Driving Intelligence to General Intelligence
• Detailed Parameters of EAI "Cerebrum" SoC
• Detailed Parameters of EAI "Cerebellum" SoC
• EAI "Cerebrum" SoC: R1
• Intelligent Control Cerebellum SoC: D9-MAX
• Intelligent Control Cerebellum SoC D9-MAX: Application Solution and Development Kit
• Detailed Parameters of High-Performance MCU for EAI
• Joint Module Solution Based on E3119
• Dexterous Hand Solution Based on E3116
• LiDAR Solution Based on E3118
• 5.2 Rockchip
• Profile
• Evolution and Future Development of EAI Chips
• Parameters of RK3588 Series Products
• Parameters of RK182X Co-processor SoC & RV1126B Image Processor
• RK182X Series Co-Processor SoCs and Application Solutions
• RK3588
• RK3588 Series Application Solution and Future Planning
• RK3588 Application Solution: Advantech?Reinforced Vision Controller
• RK3588 Application Solution: High-Performance AMR Robot Core Computing Platform Solution
• RK3588 Development Toolchain: RKNN-Toolkit2
• 5.3 D-Robotics
• Evolution and Future Development of EAI Chips
• Parameters of EAI SoC Products
• Parameters of EAI Developer Kit Product
• Sunrise 5 Intelligent Computing Chip, CPU+BPU Heterogeneous Architecture
• Intelligent Computing Chip Application Ecosystem: NIU Electric Two-Wheeler Smart Mobility
• Developer Kit Application Ecosystem: SENSING?Tech's GMSL2 Series Camera Module
• 5.4 Black Sesame Technologies
• Evolution and Future Development of EAI Chips
• Huashan A2000 (1)
• Huashan A2000 (2)
• SesameX EAI Computing Platform Module
• Huashan A2000
• Huashan A2000: Adopt Self-Developed Jiushao Architecture NPU Core
• Huashan A2000: Efficient, Easy-to-use Shanhai AI Toolchain
• SesameX: Full-Stack Robot Platform System
• 5.5 Cambricon
• Evolution and Future Development of EAI Chips
• Detailed Parameters of EAI Chips (1)
• Detailed Parameters of EAI Chips (2)
• Siyuan 590: Self-Developed Intelligent Processor Microarchitecture MLUarch05
• AI Computing Library: Cambricon CNNL
• Computer Vision Library: CNCV
• Software Development Platform: Cambricon NeuWare
• MLU Inference Acceleration Engine: MagicMind
• 5.6 NVIDIA
• Profile
• EAI SoC Series and Evolution
• Mainstream On-device Computing Chip for Humanoid Robots: Jetson Orin
• Detailed Parameters of Jetson Orin
• Mainstream On-device Computing Chip for Humanoid Robots: Jetson Thor
• Detailed Parameters of Jetson Thor
• NVIDIA Jetson Thor: Adopt Blackwell Architecture for GPU
• NVIDIA Jetson Thor: NVIDIA Metropolis for Vision AI Agents
• NVIDIA Jetson Thor: NVIDIA Holoscan for Sensor Processing to Realize Real-Time Data Stream Transmission
• NVIDIA Jetson Thor: JetPack 7 Provides Complete Tools and Libraries for Building AI Edge Applications
• NVIDIA Jetson Thor: Collaborate with Isaac Open-Source Robot Platform
• NVIDIA DreamZero World Action Model (WAM)
• NVIDIA DreamZero World Action Model (WAM): Architecture
• NVIDIA DreamZero World Action Model (WAM): Advantages
• Open Multimodal Model: Nemotron 3 Nano Omni Model
• 5.7 Qualcomm
• Evolution and Future Development of EAI Chips
• Detailed Parameters of Dragonwing Series Chips: IQ10, IQ9
• Detailed Parameters of Dragonwing Series Chips: IQ8, IQ6, QCS8550
• IQ10 Series
• QCS8550 Application Solution: Robrain AI Robot Solution
• 5.8 Intel
• EAI SoC Series and Evolution
• Parameter Comparison between Core Ultra Series Products
• Detailed Parameters of Intel Core i7 Series
• Detailed Parameters of Intel Core i5 Series
• On-device Robot Computing Chip: 3rd Generation Intel Core Ultra
• 3rd Generation Intel Core Ultra: 18A Process
• 3rd Generation Intel Core Ultra GPU Architecture: Xe3
• 3rd Generation Intel Core Ultra Equipped with NPU 5: Optimized Specifically for AI Tasks
• 5.9 MediaTek
• Evolution and Future Development of EAI Chips
• Genio Pro, Genio 420, Genio 360
• Dimensity 9400, Dimensity 9400+
• Genio Pro
• Genio 420
• Genio 360
• Support MediaTek NeuroPilot AI Software Development Kit
• 5.10 Li Auto
• Parameters of Mach M100
• Self-Developed Chip Mach M100
• Self-Developed Chip Mach M100: Internal Structure
• Self-Developed Chip Mach M100: CPU Structure
• Self-Developed Chip Mach M100: NPU Structure
• 5.11 HOUMO.AI
• Evolution and Future Development of Embodied Intelligence Chips
• Houmo Manjie M50 Chip (1)
• Houmo Manjie M50 Chip (2)
• Houmo Manjie M50: Equipped with the "Tianxuan" Architecture - Self-developed Second-Generation Compute-in-Memory IPU Design
• Houmo Manjie M50 Toolchain: Houmo Dadao
• 5.12 Huixi Intelligent Technology
• Evolution and Future Development of Embodied Intelligence Chips
• Huixi R1 (1)
• Huixi R1 (2)
• Self-developed Turing-Complete Instruction Set
• Self-developed RPU Neural Network Accelerator
• Innovative Functional Safety Architecture RIF