Search results for: 'artificial intelligence and cloud computing'

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...

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...

This research explores the powerful global forces that will reshape business, society, and technology over the coming decade, offering a holistic view of the megatrends redefining the future operating environment. These forces span geopolitical fragmentation, demographic divergence, technological acceleration, environmental pressures, and fundament...

Multimodal sensor fusion is an advanced perception technology that combines data from multiple sensors, including cameras, LiDAR, radar, ultrasonic sensors, thermal cameras, and inertial measurement units (IMUs), to give robots and autonomous systems a more accurate, reliable, and comprehensive understanding of their environment. It overcomes the l...

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...

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...

The study examines the global industrial control systems market, structured around three core technology segments: distributed control systems (DCS), programmable logic controllers (PLCs), and SCADA. Using 2025 as the base year, the research provides market sizing and a seven-year forecast by region, industry vertical, and offering type, including ...