Analog AI to Disrupt Acceleration Hardware in the Near-Term
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 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
1.0 Strategic Imperatives
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
2.0 Growth Environment
2.1 Research Scope
2.2 Research Findings
3.0 AI Accelerators - Technology Landscape
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
4.0 AI Accelerators - R&D Trends
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
5.0 AI Accelerators - Funding Trends
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
6.0 AI Accelerators - Market Impact and Technology Roadmap
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
7.0 AI Accelerators - Companies to Watch
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
8.0 Growth Opportunity Universe
8.1 Growth Opportunity 1: AI Accelerators to Witness High Demand from the Automotive Industry
8.2 Growth Opportunity 2: Smart Cities Provide Collaboration Opportunities for AI Accelerator Start-ups
8.3 Growth Opportunity 3: AI Chipsets to Accelerate Collaborations in Healthcare and Pharmaceutical
9.0 Industry Contacts
9.1 Key Contacts
10.0 Next Steps
10.1 Your Next Steps
- Legal Disclaimer
Companies Mentioned (Partial List)
A selection of companies mentioned in this report includes, but is not limited to:
- Untether AI, US
- SiMa.ai, US
- Hailo, Israel
- IBM Research AI Hardware Center, US
- Graphcore, UK
