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Growth Opportunities Across Next-generation AI Computing

The Technologies Reshaping AI Computing

The rapid expansion of artificial intelligence is increasing demands on computing infrastructure across the cloud-to-edge continuum. Growing model complexity, data volumes, real-time processing requirements, and power constraints are challenging conventional architectures and accelerating the development of new approaches to AI compute.

From light-speed data movement and optical acceleration in AI data centers to ultra-low-power intelligence at the edge, organizations must determine which technologies can deliver the performance, efficiency, and scalability required for the next generation of AI systems.

Access the Microelectronics Growth Portfolio

This Frost & Sullivan bundle examines two technology areas shaping AI computing:

 
 

Photonic & Optical Computing Chips for AI and Data Center Acceleration

Photonic integrated circuits (PICs), optical I/O chiplets, co-packaged optics, optical interconnects, and emerging optical computing architectures are addressing bandwidth, latency, power, and scalability constraints across AI training and inference infrastructure.

 

Neuromorphic & In-memory Chips for Edge AI Acceleration

Brain-inspired neuromorphic architectures and processing-in-memory technologies are reducing data movement and enabling low-power, low-latency intelligence across IoT, wearables, robotics, automotive Advanced Driver Assistance Systems (ADAS), and other always-on edge applications.

Together, these analyses provide visibility into technology evolution, commercialization readiness, enabling ecosystems, and emerging growth opportunities across next-generation AI computing.

The Decisions Microelectronics Leaders Must Make Now

 
1

Technology Readiness & Scalability

Which emerging computing architectures are progressing from R&D and pilot deployments toward scalable commercial adoption?

2

Performance & Power Efficiency

Where can new chip architectures overcome bandwidth, memory, latency, and power constraints limiting conventional AI computing?

3

Application & Deployment Potential

Which technologies are best positioned for AI data centers, training and inference, automotive systems, robotics, Internet of Things (IoT), and other edge AI applications?

4

Innovation & Investment Priorities

Where should semiconductor companies focus R&D, ecosystem partnerships, infrastructure investment, and commercialization strategies?

 

Why This Matters Now

Microelectronics leaders are:

01
Addressing AI infrastructure bottlenecks as compute and data movement requirements continue to increase
02
Reducing energy and latency constraints across data center and edge environments
03
Evaluating emerging architectures against established semiconductor and accelerator technologies
04
Building the right ecosystems across materials, foundries, packaging, chip design, systems, and infrastructure
05
Identifying commercialization opportunities as next-generation computing technologies move toward wider deployment

Understanding where these architectures can create measurable performance and efficiency advantages will be critical to identifying the next growth

Explore the technologies, applications, commercialization pathways, and innovation ecosystems shaping the future of AI computing.