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.
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Table of Contents
1. Types of AI Chip Products
2. Characteristics and Development Trends 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. Analyst's 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
Companies Mentioned (Partial List)
A selection of companies mentioned in this report includes, but is not limited to:
- AMD
- Apple
- IBM
- Intel
- Nvidia
- Samsung
- Supermicro
- Tesla
- TSMC
- Volkswagen
- Volvo
- Xilinx
Methodology
Primary research with a holistic, cross-domain approach
The exhaustive primary research methods are central to the value that the analyst delivers. A combination of questionnaires and on-site visits to the major manufacturers provides a first view of the latest data and trends. Information is subsequently validated by interviews with the manufacturers' suppliers and customers, covering a holistic industry value chain. This process is backed up by a cross-domain team-based approach, creating an interlaced network across numerous interrelated components and system-level devices to ensure statistical integrity and provide in-depth insight.
Complementing primary research is a running database and secondary research of industry and market information. Dedicated research into the macro-environmental trends shaping the ICT industry also allows the analyst to forecast future development trends and generate foresight perspectives. With more than 20 years of experience and endeavors in research, the methods and methodologies include:
Method
- Component supplier interviews
- System supplier interviews
- User interviews
- Channel interviews
- IPO interviews
- Focus groups
- Consumer surveys
- Production databases
- Financial data
- Custom databases
Methodology
- Technology forecasting and assessment
- Product assessment and selection
- Product life cycles
- Added value analysis
- Market trends
- Scenario analysis
- Competitor analysis
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