Edge AI Ecosystem Shaping: Constructing New Software Service Channels (pre-order)
In response to the growing need for real-time inference results in various AI application scenarios, the traditional practice of running inference in the cloud has gradually become inadequate in meeting the requirements for immediacy. Consequently, the industry has begun advocating for the relocation of inference workloads to the edge, enabling them to be executed in close proximity to where they are needed. This paradigm is known as Edge Artificial Intelligence (Edge AI). In recent years, many international brands have actively entered the Edge AI space, highlighting its significance in the market. Moreover, Edge AI differs slightly from the traditional information software industry in terms of sales channels and development models. Therefore, existing software companies and information service providers need to make adjustments to seize the growth opportunities in this evolving landscape.
1. THE DRIVE FOR TIMELINESS: SHAPING EDGE AI
1.1 Natural Technological Evolution and Practical Demands in Edge AI
1.2 Edge AI's Resemblance to Edge Computing and Its Advantages
1.3 Edge AI Weaknesses Compared to Cloud AI
2. GLOBAL HARDWARE AND SOFTWARE BRANDS' AGGRESSIVE EXPANSION INTO EDGE AI
2.1 Chipmakers' Strategies in Edge AI
2.2 Startups in the Edge AI Space
2.3 Major Cloud Providers' Involvement in Edge AI
3. EDGE AI DEVELOPMENT ADAPTING TO MULTIPLE NEW SOFTWARE MODELS
3.1 Cloud-Based Edge AI Development Tools
3.2 Diverse Approaches to Edge AI Inference Software: Embedded and Marketplace Routes
3.3 Diverse Approaches and Billing Methods in Edge AI Deployment
4.MIC PERSPECTIVE
Appendix
LIST OF COMPANIES
1.1 Natural Technological Evolution and Practical Demands in Edge AI
1.2 Edge AI's Resemblance to Edge Computing and Its Advantages
1.3 Edge AI Weaknesses Compared to Cloud AI
2. GLOBAL HARDWARE AND SOFTWARE BRANDS' AGGRESSIVE EXPANSION INTO EDGE AI
2.1 Chipmakers' Strategies in Edge AI
2.2 Startups in the Edge AI Space
2.3 Major Cloud Providers' Involvement in Edge AI
3. EDGE AI DEVELOPMENT ADAPTING TO MULTIPLE NEW SOFTWARE MODELS
3.1 Cloud-Based Edge AI Development Tools
3.2 Diverse Approaches to Edge AI Inference Software: Embedded and Marketplace Routes
3.3 Diverse Approaches and Billing Methods in Edge AI Deployment
4.MIC PERSPECTIVE
Appendix
LIST OF COMPANIES
LIST OF TABLES
Table 1: A Comparison of Characteristics between Cloud AI and Edge AI
Table 1: A Comparison of Characteristics between Cloud AI and Edge AI
LIST OF FIGURES
Figure 1: Evolutionary Timeline of Edge Artificial Intelligence Technologies
Figure 2: Architecture and Principles of Horizontal Federated Learning Systems
Figure 3: Intel's Integration of OpenVINO Software to Support AI Acceleration Chips
Figure 4: Illustration of AWS IoT Greengrass ML Inference Service
Figure 5: SensiML SaaS Billing Model
Figure 6: Edge AI Utilizing Cloud Application Marketplace Delivery and Update Mechanisms
Figure 1: Evolutionary Timeline of Edge Artificial Intelligence Technologies
Figure 2: Architecture and Principles of Horizontal Federated Learning Systems
Figure 3: Intel's Integration of OpenVINO Software to Support AI Acceleration Chips
Figure 4: Illustration of AWS IoT Greengrass ML Inference Service
Figure 5: SensiML SaaS Billing Model
Figure 6: Edge AI Utilizing Cloud Application Marketplace Delivery and Update Mechanisms
