Big Changes Ahead For Inside Auto Cabins


The space we occupy inside our vehicles is poised to change from mere enclosure to participant in the driving experience. Whether for safety or for comfort, a wide range of sensors are likely to appear that will monitor the “contents” of the vehicle. The overall approach is referred to as an in-cabin monitoring system (ICMS), but the specific applications vary widely. “In-cabin sensing... » read more

Improving Energy And Power Efficiency In The Data Center


Energy costs in data centers are soaring as the amount of data being generated explodes, and it's being made worse by an imbalance between increasingly dense processing elements that are producing more heat and uneven server utilization, which requires more machines to be powered up and cooled. The challenge is to maximize utilization without sacrificing performance, and in the past that has... » read more

Why It’s So Difficult — And Costly — To Secure Chips


Rising concerns about the security of chips used in everything from cars to data centers are driving up the cost and complexity of electronic systems in a variety of ways, some obvious and others less so. Until very recently, semiconductor security was viewed more as a theoretical threat than a real one. Governments certainly worried about adversaries taking control of secure systems through... » read more

Will Markets For ML Models Materialize?


Developers are spending increasing amounts of time and effort in creating machine-learning (ML) models for use in a wide variety of applications. While this will continue as the market matures, at some point some of these efforts might be seen as reinventing models over and over. Will developers of successful models ever have a marketplace in which they can sell those models as IP to other d... » read more

Gaps In The AI Debug Process


When an AI algorithm is deployed in the field and gives an unexpected result, it's often not clear whether that result is correct. So what happened? Was it wrong? And if so, what caused the error? These are often not simple questions to answer. Moreover, as with all verification problems, the only way to get to the root cause is to break the problem down into manageable pieces. The semico... » read more

Raising The Bar With The Next Generation Of AI For Chip Design


The semiconductor industry is enjoying renewed growth despite chip shortages plaguing everything from cars to kitchen appliances. But while the chips themselves continue to get faster and smarter, the chip design process itself hasn’t changed that much in 20+ years. It typically takes 2-3 years to design a chip with a large engineering team and tens or hundreds of millions of dollars to get a... » read more

Absence of Barren Plateaus in Quantum Convolutional Neural Networks


Abstract:  Quantum neural networks (QNNs) have generated excitement around the possibility of efficiently analyzing quantum data. But this excitement has been tempered by the existence of exponentially vanishing gradients, known as barren plateau landscapes, for many QNN architectures. Recently, quantum convolutional neural networks (QCNNs) have been proposed, involving a sequence of convol... » read more

A Broad Look Inside Advanced Packaging


Choon Lee, chief technology officer of JCET, sat down with Semiconductor Engineering to talk about the semiconductor market, Moore’s Law, chiplets, fan-out packaging, and manufacturing issues. What follows are excerpts of that discussion. SE: Where are we in the semiconductor cycle right now? Lee: If you look at 2020, it was around 10% growth in the overall semiconductor industry. ... » read more

AI/ML Workloads Need Extra Security


The need for security is pervading all electronic systems. But given the growth in data-center machine-learning computing, which deals with extremely valuable data, some companies are paying particular attention to handling that data securely. All of the usual data-center security solutions must be brought to bear, but extra effort is needed to ensure that models and data sets are protected ... » read more

HBM2E Raises The Bar For Memory Bandwidth


AI/ML training capabilities are growing at a rate of 10X per year driving rapid improvements in every aspect of computing hardware and software. HBM2E memory is the ideal solution for the high bandwidth requirements of AI/ML training, but entails additional design considerations given its 2.5D architecture. Designers can realize the full benefits of HBM2E memory with the silicon-proven memory s... » read more

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