Search results for: 'intelligence at three outlook on edge computing 2014'

The October 2025 edition of the CST Technology Opportunity Engine highlights transformative innovations shaping the future of edge computing. The study explores the evolution of decentralized data processing models that enable ultra-low latency, localized intelligence, and real-time decision-making across industries.Driven by the convergence of IoT...

This study focuses on heterogeneous computing architectures for automotive and robotics applications, examining how centralized and zonal compute architectures, heterogeneous SoCs integrating CPUs, GPUs, NPUs, DSPs, FPGAs, and dedicated safety islands, as well as robotics edge-AI controllers and mixed-criticality computing platforms, are enabling t...

The study evaluates photonic and optical computing technologies as critical enablers of next-generation AI infrastructure, addressing growing bandwidth, latency, power consumption, and scalability constraints associated with copper-based interconnects. The analysis covers photonic integrated circuits (PICs), optical I/O chiplets, co-packaged optics...

This study examines the emerging landscape of neuromorphic computing and its role in enabling low-power, real-time, brain-inspired intelligence across edge and autonomous systems. It reviews advancements in spiking neural networks, memristor-based computing in memory, event-driven vision sensors, and hybrid neuromorphic and conventional architectur...

The escalating compute and power demands of edge AI are exposing the limits of conventional von Neumann architectures. Manufacturers deploying always-on sensing, real-time inference, and autonomous decision-making at the edge are constrained by the energy, latency, and memory-bandwidth ceilings of standard NPUs and MCUs. Traditionally, edge AI work...

This Frost & Sullivan research service analyzes the global industrial automation market from 2020 to 2025, with 2020 as the base year. The deliverable offers an in-depth look at the dynamics influencing and challenging the growth of the industrial automation market and analyzes the process automation technology segments across process, discrete, an...

Micro-sensors are no longer just components that capture data—they are becoming the intelligence behind connected products and autonomous systems. As industries demand faster decisions, greater reliability, and continuous monitoring, sensing platforms are evolving by combining embedded AI, advanced materials, low-power design, and connected archite...

Intelligent automation is driven by artificial intelligence (AI) technologies. In industrial operations, AI acts as the catalyst to increase the productivity in the operations. Three types of artificial technology are identified as a game-changer in industrial operations in which these technologies are digitalizing and streamlining the value chain ...

AI maturity, implementation, and AI-enabled or AI-augmented operations are increasing year over year, moving from pilot to production. Because AI delivers the biggest business outcomes, it is a top outcome driver. The impact of AI can be seen in quality control and computer vision in smart manufacturing as well. AI-powered visual inspection transfo...