Search results for: 'plam study'

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 rapid expansion of Internet of Things (IoT) ecosystems has intensified the demand for uninterrupted, autonomous sensing solutions that can operate without reliance on conventional batteries. This research study investigates the evolving landscape of self-powered sensors as a foundational technology for next-generation, batteryless IoT—particula...

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 eCommerce and logistics industries are entering a period of accelerated transformation driven by the need for higher throughput, improved accuracy, and real-time operational intelligence. Emerging sensor technologies are becoming essential to support this shift as fulfillment centers, transport networks, and last-mile operations manage rising c...

This study focuses on how next-generation chiplet architectures, high-bandwidth memory (HBM), and advanced packaging technologies such as 2.5D/3D integration and fan-out wafer-level packaging are enabling the next wave of AI and high-performance computing (HPC) system architectures, rather than providing a broad packaging market overview. It analyz...

This study examines the technology landscape, commercialization progress, and future growth opportunities of wide-bandgap (WBG) semiconductor materials, with a primary focus on silicon carbide (SiC), gallium nitride (GaN), and gallium oxide (Ga₂O₃) for power and RF electronics. It evaluates how these materials enable higher efficiency, greater powe...

Disruptive AI models are increasingly challenging traditional assumptions about “classic” cloud data centers. As training clusters scale up to thousands of accelerators and consume tens of megawatts of power, the conventional CPU-centric, air-cooled, monolithic server model is no longer sustainable. This research study explores the megatrend of AI ...

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...

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...