1.1 Research Scope

1.2 Research Methodology

1.3 Research Methodology Explained

1.4 Key Findings

2.1 Overview of AI on Edge

2.2 Benefits of AI at the Edge

2.3 Distributed AI Improves Operational Timeliness and Reduces Privacy Risks

2.4 Specific Example: Distributed AI at the Edge

3.1 Rapid Migration of AI Inference Workloads to the Edge is Driving the Edge AI Chipsets Market

3.2 AI on Edge helps to Overcome the Challenges Associated with Cloud Computing

4.1 The Transformative Impact of Edge AI Cuts down Latency across Domains, Helping Companies take Faster Decisions

4.2 Automotive Participants are Making Efforts to Unlock Higher Levels of Autonomy using Edge AI Technology

4.3 With the Advent of Edge AI, Brick and Mortar Stores Now have Advanced Tools to Stay Ahead against Online Shopping

4.4 Edge AI in Supply Chains is Being Utilized to Predict Consumer Demand and Reduce Inventory Costs

4.5 Case Example 1: Edge AI based Analytics for Business Management

4.6 Case Example 2: Edge AI based Analytics for Predictive Maintenance

5.1 Companies to Watch – Company 1: LGN.ai

5.2 Companies to Watch – Company 2: Horizon Robotics

5.3 Companies to Watch – Company 3: NVIDIA

5.4 Companies to Watch – Company 4: Intel

5.5 Companies to Watch – Company 5: IBM

5.6 Companies to Watch – Company 6: Qualcomm

5.7 Companies to Watch – Company 7: Google

5.8 Companies to Watch – Company 8: Imagimob

5.9 Companies to Watch – Company 9: Xnor.ai

5.10 Companies to Watch – Company 10: Gorilla Technology

6.1 Participants in the Ecosystem are Partnering to Accelerate the Adoption of AI on the Edge

6.2 Venture Capitalists are Investing Aggressively in Promising start-ups Offering AI capabilities at the Edge

7.1 Will Edge Computing Replace Cloud: Business Perspective

7.2 Edge Computing is a Promising Solution to Support Computation-intensive AI Applications in Resource Constrained Environments

8.1 Key Contacts

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