Technology Market Penetration and Roadmap for AI Accelerators

Technology Market Penetration and Roadmap for AI Accelerators

Analog AI to Disrupt Acceleration Hardware in the Near-Term

RELEASE DATE
08-Dec-2021
REGION
Global
Research Code: DA2B-01-00-00-00
SKU: ES01202-GL-TR_26045
$4,950.00
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$4,950.00
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Description

The proliferation of internet of things applications, such as smart manufacturing and smart transportation, has resulted in the explosion of artificial intelligence (AI) and big data. These applications heavily rely on complex AI and machine learning algorithms, requiring computational solutions to handle varying workloads. Power-intensive, costly, and legacy hardware such as central processing units limit the wide deployment of AI solutions. The demand for energy-efficient AI acceleration hardware at low capital costs is high.

According to Moore’s law, the number of transistors on a chipset is set to double every two years, boosting computational devices’ speed and performance capabilities. The conventional method to satisfy Moore’s law is by shrinking transistors. However, engineers are finding it increasingly difficult to reduce the size of transistors. AI acceleration hardware built upon traditional chipset architecture appears to be approaching a bottleneck due to design limitations. Stakeholders are forced to develop next-generation AI acceleration hardware architecture, resulting in performance disruption.

This Frost & Sullivan technology and innovation report offers insights and growth opportunities for AI acceleration hardware or AI accelerators. The research scope focuses on the benefits and applications of AI accelerators and covers the following:
• Technology landscape and roadmap
• Research and development trends
• Funding trends
• Regional trends
• Stakeholder ecosystem
• Growth drivers and restraints

Table of Contents

1.1 Why Is It Increasingly Difficult to Grow?The Strategic Imperative 8™: Factors Creating Pressure on Growth

1.2 The Strategic Imperative 8™

1.3 The Impact of the Top Three Strategic Imperatives on AI Accelerators

1.4 Growth Opportunities Fuel the Growth Pipeline Engine™

1.5 Research Methodology

1.5 Research Methodology (continued)

2.1 Research Scope

2.2 Research Findings

3.1 AI Accelerators—Technology Overview

3.2 AI Accelerators Enable Heterogeneous Processing to Handle Varying AI Workloads

3.3 Growth Drivers

3.4 Growth Restraints

4.1 At-Memory Computing and Energy Efficiency Emerge as Key Innovation Areas

4.2 The Patent Landscape Suggests High Focus on Improved AI Hardware for Heterogeneous Processing

4.3 The US Jurisdiction Dominates Patent Activities in AI Acceleration

4.4 Analog AI to Overcome Data Shuffling Bottlenecks

5.1 Strategic Investors to Commercialize Green Edge AI Accelerators

5.2 Strategic Investors Eyeing Custom Dataflow Architecture

5.3 Corporate Ventures Exhibit Interest in Promising AI Acceleration Start-ups

6.1 New-Age NLP and Vision Systems Drive Innovations in AI Accelerators

6.2 The US Offers High Growth Opportunities for AI Accelerator Start-ups

6.3 AI-on-5G Chipsets to Become a Reality in the Medium Term

7.1 Untether AI, US

7.2 SiMa.ai, US

7.3 Hailo, Israel

7.4 IBM Research AI Hardware Center, US

7.5 Graphcore, UK

8.1 Growth Opportunity 1: AI Accelerators to Witness High Demand from the Automotive Industry

8.1 Growth Opportunity 1: AI Accelerators to Witness High Demand from the Automotive Industry (continued)

8.2 Growth Opportunity 2: Smart Cities Provide Collaboration Opportunities for AI Accelerator Start-ups

8.2 Growth Opportunity 2: Smart Cities Provide Collaboration Opportunities for AI Accelerator Start-ups (continued)

8.3 Growth Opportunity 3: AI Chipsets to Accelerate Collaborations in Healthcare and Pharmaceutical

8.3 Growth Opportunity 3: AI Chipsets to Accelerate Collaborations in Healthcare and Pharmaceutical (continued)

9.1 Key Contacts

10.1 Your Next Steps

10.2 Why Frost, Why Now?

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Related Research
The proliferation of internet of things applications, such as smart manufacturing and smart transportation, has resulted in the explosion of artificial intelligence (AI) and big data. These applications heavily rely on complex AI and machine learning algorithms, requiring computational solutions to handle varying workloads. Power-intensive, costly, and legacy hardware such as central processing units limit the wide deployment of AI solutions. The demand for energy-efficient AI acceleration hardware at low capital costs is high. According to Moore’s law, the number of transistors on a chipset is set to double every two years, boosting computational devices’ speed and performance capabilities. The conventional method to satisfy Moore’s law is by shrinking transistors. However, engineers are finding it increasingly difficult to reduce the size of transistors. AI acceleration hardware built upon traditional chipset architecture appears to be approaching a bottleneck due to design limitations. Stakeholders are forced to develop next-generation AI acceleration hardware architecture, resulting in performance disruption. This Frost & Sullivan technology and innovation report offers insights and growth opportunities for AI acceleration hardware or AI accelerators. The research scope focuses on the benefits and applications of AI accelerators and covers the following: • Technology landscape and roadmap • Research and development trends • Funding trends • Regional trends • Stakeholder ecosystem • Growth drivers and restraints
More Information
No Index No
Podcast No
Author Arjun Mehta
Industries Electronics and Sensors
WIP Number DA2B-01-00-00-00
Is Prebook No