25G Ethernet is rapidly becoming the preferred standard for real-time AI data movement at the edge, balancing bandwidth, latency, cost, and power efficiency. Its adoption across automotive, industrial...
Recent research from KAUST and Compumacy introduces a co-optimization framework that enhances in-memory AI accelerators' versatility by jointly optimizing hardware design, workload characteristics, an...
Marvell Technology’s recent earnings beat and optimistic fiscal 2028 revenue forecast highlight a transformative shift in AI infrastructure toward diversified, custom silicon and integrated connectivi...
Marvell Technology’s $5.5 billion investment in networking and security silicon marks a strategic shift in AI infrastructure, emphasizing connectivity and chip-level security alongside compute. This m...
Recent advances in AI chip security, optical interconnects, and ultra-high bandwidth memory are driving a fundamental shift in semiconductor design. These innovations address emerging AI integrity thr...
Unified AI infrastructure at the edge integrates compute, connectivity, and power management within micro data centers, enabling real-time AI workloads with ultra-low latency and improved power effici...
The rapid growth of AI workloads is intensifying strain on US power grids, prompting a shift from centralized hyperscale data centers to distributed edge and micro data centers. Innovations in power-e...
As AI workloads become more intense and mission-critical, data centers are undergoing a fundamental redesign to support high power densities, advanced cooling, and environmental sustainability. This a...
The rapid growth of AI workloads is significantly increasing data center power consumption, putting strain on the U.S. electrical grid. This analysis explores how AI's unique power demands challenge t...
In 2026, AI infrastructure is undergoing a transformative shift driven by NVIDIA's NVFP4 low-precision training format, the expansion of edge and micro data centers, and innovative high-density data c...