Advancements in AI on Edge - Emerging Applications and Innovations

Advancements in AI on Edge - Emerging Applications and Innovations

An Insight Into How AI On Edge Is Likely To Open Up New Opportunities For Businesses In The Near Future

RELEASE DATE
26-Dec-2019
REGION
Global
Research Code: D92E-01-00-00-00
SKU: IT03995-GL-TR_23956

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$4,950.00

$3,712.50 save 25 %

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Description

Traditional cloud computing models sends data from the device to the cloud for data analysis and the decision is sent back to the device for implementation. The agility of cloud computing is great but not enough to overcome certain challenges such as latency, bandwidth, processing the data for real-time decision making, costs associated with data transfer between cloud and edge. Cloud AI models often needed to be trained with data collected from devices, making it difficult and time consuming to apply AI and generate insights. AI with edge computing will solve the challenges faced in cloud, as the inference and training is totally moved towards the devices.

In brief, this research provides the following:

• A brief snapshot of convergence of edge computing with AI
• The challenges of existing cloud AI models and how edge can solve
• Key participants delivering intelligent edge AI solutions for different industries
• Highlights of innovative future applications through convergence models
• Roadmap and key milestones to achieve in the near, medium and long term to make devices, machines and things more intelligent.

Table of Contents

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

Legal Disclaimer

Traditional cloud computing models sends data from the device to the cloud for data analysis and the decision is sent back to the device for implementation. The agility of cloud computing is great but not enough to overcome certain challenges such as latency, bandwidth, processing the data for real-time decision making, costs associated with data transfer between cloud and edge. Cloud AI models often needed to be trained with data collected from devices, making it difficult and time consuming to apply AI and generate insights. AI with edge computing will solve the challenges faced in cloud, as the inference and training is totally moved towards the devices. In brief, this research provides the following: • A brief snapshot of convergence of edge computing with AI • The challenges of existing cloud AI models and how edge can solve • Key participants delivering intelligent edge AI solutions for different industries • Highlights of innovative future applications through convergence models • Roadmap and key milestones to achieve in the near, medium and long term to make devices, machines and things more intelligent.
More Information
No Index No
Podcast No
Author Naga Avinash Gunturu
Industries Information Technology
WIP Number D92E-01-00-00-00
Is Prebook No