Development Trends of AI Chip Products and Strategies of Leading Brands (pre-order)
March 8, 2023
Market Intelligence & Consulting Institute (MIC)
19
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Development Trends of AI Chip Products and Strategies of Leading Brands (pre-order)
Abstract Development Trends of AI Chip Products and Strategies of Leading Brands (pre-order)
Executive Summary:
Neural network chip technology is a branch of AI (Artificial Intelligence) that uses large-scale ICs (Integrated Circuits) to simulate the neural patterns of the human brain in a systematic way. The technology can guide computers to process data in a way that is similar to a human brain. The neural pattern system is modeled after how the biological neural system is made up, how signals are transmitted, and how it processes and stores information. This involves electronic circuit materials, components, circuit simulation, circuit design, computing architecture, algorithms, and system engineering simulation. This report focuses on three areas: types of AI chips, characteristics of neural network chips, and strategies of major brands.
Content
Table of Contents:
1.Types of AI Chip Products 2.Characteristics and DevelopmentTrends of Neural Network Chips 2.1 Neural Network Systems Can Mimic Human Brain Using VLSI 2.2 Neural Network Chips Enables More Powerful AI Applications Through Deep Learning Algorithms 3.Strategies of Leading Brands in Different Applications 3.1 GPU-centric NVIDIA Xavier Chip Dedicated to Supporting Autonomous Driving 3.2 AMD Instinct Chips Committed to Improving Computing Performance 3.3 The Acquisition of Xilinx by AMD Helps Fill AMD's FPGA Product Gap 3.4 Intel Launches Agilex with F/I/M Series Targeting Different Applications Following the Acquisition of Altera 3.5 Intel Introduces NPU Products to Collaborate with Partners for the Development of Neural Network Computing 3.6 Apple and Samsung Incorporate NPU in Their Mobile Processors 3.7 Tesla's Autonomous Driving Chips Uses an NPU as Computing Core 4. MIC Perspective Appendix List of Companies List of Tables: Table 1 Classification of AI Chip products by Application and Computing Mode Table 2 Simulation of Silicon Neurons Mimicking Brain Neuron Operations Table 3 AI Processor Types Table 4 Autonomous Driving Processors of Nvidia Table 5 Comparison of AMD CDNA Architecture GPU Products Table 6 Intel Agilex Chips Table 7 Comparison of Apple AI ICs and Samsung SoCs
List of Figures: Figure 1 The Illustration of Biological Neural Systems and Neural Network Systems Figure 2 The Role of Neural Network and Deep Learning in AI Figure 3 Thee Features of Deep Learning Figure 4 Nvidia Xavier Chip Architecture Figure 5 The Illustration of Apple Smartphone SoC Configuration Figure 6 Tesla Hardware 3 FSD and Chip Architecture
Development Trends of AI Chip Products and Strategies of Leading Brands (pre-order)
Executive Summary:
Neural network chip technology is a branch of AI (Artificial Intelligence) that uses large-scale ICs (Integrated Circuits) to simulate the neural patterns of the human brain in a systematic way. The technology can guide computers to process data in a way that is similar to a human brain. The neural pattern system is modeled after how the biological neural system is made up, how signals are transmitted, and how it processes and stores information. This involves electronic circuit materials, components, circuit simulation, circuit design, computing architecture, algorithms, and system engineering simulation. This report focuses on three areas: types of AI chips, characteristics of neural network chips, and strategies of major brands.
Table of Contents:
1.Types of AI Chip Products 2.Characteristics and DevelopmentTrends of Neural Network Chips 2.1 Neural Network Systems Can Mimic Human Brain Using VLSI 2.2 Neural Network Chips Enables More Powerful AI Applications Through Deep Learning Algorithms 3.Strategies of Leading Brands in Different Applications 3.1 GPU-centric NVIDIA Xavier Chip Dedicated to Supporting Autonomous Driving 3.2 AMD Instinct Chips Committed to Improving Computing Performance 3.3 The Acquisition of Xilinx by AMD Helps Fill AMD's FPGA Product Gap 3.4 Intel Launches Agilex with F/I/M Series Targeting Different Applications Following the Acquisition of Altera 3.5 Intel Introduces NPU Products to Collaborate with Partners for the Development of Neural Network Computing 3.6 Apple and Samsung Incorporate NPU in Their Mobile Processors 3.7 Tesla's Autonomous Driving Chips Uses an NPU as Computing Core 4. MIC Perspective Appendix List of Companies List of Tables: Table 1 Classification of AI Chip products by Application and Computing Mode Table 2 Simulation of Silicon Neurons Mimicking Brain Neuron Operations Table 3 AI Processor Types Table 4 Autonomous Driving Processors of Nvidia Table 5 Comparison of AMD CDNA Architecture GPU Products Table 6 Intel Agilex Chips Table 7 Comparison of Apple AI ICs and Samsung SoCs
List of Figures: Figure 1 The Illustration of Biological Neural Systems and Neural Network Systems Figure 2 The Role of Neural Network and Deep Learning in AI Figure 3 Thee Features of Deep Learning Figure 4 Nvidia Xavier Chip Architecture Figure 5 The Illustration of Apple Smartphone SoC Configuration Figure 6 Tesla Hardware 3 FSD and Chip Architecture