Redefining XPU Memory For AI Data Centers Through Custom HBM4: Part 2


This is the second in a three-part series from Alphawave Semi on HBM4 and gives insights into HBM implementation challenges. Click here for part 1, for an overview on HBM, and in part 3, we will introduce details of a custom HBM implementation. Implementing a 2.5D System-in-Package (SiP) with High Bandwidth Memory (HBM) is a complex process that spans across architecture definition, designi... » read more

Redefining XPU Memory For AI Data Centers Through Custom HBM4: Part 1


This is the first of a three-part series on HBM4 and gives an overview of the HBM standard. Part 2 will provide insights on HBM implementation challenges, and part 3 will introduce the concept of a custom HBM implementation. Relentless growth in data consumption Recent advances in deep learning have had a transformative effect on artificial intelligence (AI) and the ever-increasing volume of ... » read more

One Chip Vs. Many Chiplets


Experts at the Table: Semiconductor Engineering sat down to discuss the growing list of challenges at advanced nodes and in advanced packages, with Jamie Schaeffer, vice president of product management at GlobalFoundries; Dechao Guo, director of advanced logic technology R&D at IBM; Dave Thompson, vice president at Intel; Mustafa Badaroglu, principal engineer at Qualcomm; and Thomas Ponnusw... » read more

Extending Chip Lifetime With Safer Voltage Scaling


What if your chips lived 20% longer without compromising performance, and even while reducing power consumption? How would it affect your product’s reliability and cost? What would be the effect on your profitability? With the demand for longer-lasting chips growing across industries, designers and reliability engineers face increasing pressure to ensure their products perform correctly fo... » read more

Outsmarting Silent Data Corruption In AI Processors With Two-Stage Detection


Silent data corruption is on the rise following advancements in semiconductor technology. The explosion in AI for speech, image, video, and text processing leads to a growing complexity and diversity of hardware systems, bringing an increased risk to data integrity. SDC rate is much higher than software engineers expect, undermining the hardware reliability they used to take for granted. Rec... » read more

Is Liquid Cooling Right For Your Data Center?


We live in an exciting time—liquid cooling, which once seemed more trouble than it’s worth, is fast becoming an accepted and sought-after technology in the data center industry. That said, it’s still a complex technology to implement, especially in legacy facilities. Is your data center ready to operationalize liquid cooling? Liquid cooling in the data center Liquid cooling in the d... » read more

Data Center Digital Twin Return On Investment From An Environmental Standpoint


Data center operators face growing pressure to enhance sustainability. Understanding where inefficiencies occur is the first step toward making impactful changes. Cadence Reality DC Digital Twin helps you identify inefficiencies, implement solutions, and track improvements. This white paper reveals how Cadence Reality DC Digital Twin can save an average of 316MWh annually, delivering a retur... » read more

From Reaction To Prevention In Data Center RAS


The rise of artificial intelligence (AI), cloud services, and IoT has fueled the rapid expansion of hyperscale data centers. These massive facilities house thousands of servers, all working to support an increasingly digital world. But as the scale of data centers grows, so too does the need for reliable and high-performance semiconductors. Semiconductor failures and inconsistencies can cause s... » read more

Redefining RAS in Datacenters with Real-Time Health Monitoring


Abstract Hyperscale datacenters require intense computational power for compute-intensive tasks, such as AI, data analytics, machine learning, and big data processing. They leverage parallel processing across multiple computers, in high-density servers, to handle complex tasks efficiently. This uses specialized, powerful processors and training and inference of specific GPUs or ASICs. Such c... » read more

The Cost Of EDA Data Storage And Processing Efficiency


Engineering teams are turning to the cloud to process and store increasing amounts of EDA data, but while the compute resources in hyperscale data centers are virtually unlimited, the move can add costs, slow access to data, and raise new concerns about sustainability. For complex chip designs, the elasticity of the cloud is a huge bonus. With advanced-node chips and packaging, the amount of... » read more

← Older posts