中文 English

How Inferencing Differs From Training in Machine Learning Applications


Machine learning (ML)-based approaches to system development employ a fundamentally different style of programming than historically used in computer science. This approach uses example data to train a model to enable the machine to learn how to perform a task. ML training is highly iterative with each new piece of training data generating trillions of operations. The iterative nature of the tr... » read more

Manufacturing Shifts To AI Of Things


AI is being infused into the Internet of Things, setting the stage for significant improvements in manufacturing productivity, improved uptime, and reduced costs — regardless of market segment. The traditional approach to improving manufacturing equipment reliability and efficiency is regular scheduled maintenance. While that is an improvement over just fixing or replacing equipment when i... » read more

2021 Top Tech Videos


While the world’s chip shortage dominated the 2021 headlines, the semiconductor industry blazed new trails with the increased electrification of cars, focused AI applications, improving power/performance, better utilization of data deluges, dealing with design challenges in advanced nodes and much more focus on chip security. Semiconductor Engineering’s Tech Talks reflected these focus a... » read more

Securing Short-Range Communications


Short-range wireless communication technology is in widespread use and growing rapidly, adding conveniences for consumers while also opening the door to a whole range of cyberattacks. This technology is common across a variety of applications, from wireless key fobs to unlock a car and start the ignition, to tags used to help drivers find misplaced items such as car keys. RFID also is starti... » read more

On-Chip FPGA: The “Other” Compute Resource


When system companies discuss processing requirements for their next generation products, the typical discussion invariably leads to: what should the processor subsystem look like? Do you upgrade the embedded processors in the current subsystem to the latest and greatest embedded CPU? Do you add more CPUs? Or perhaps add a little diversity by adding a DSP or GPU? One compute resource tha... » 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

Week In Review: Auto, Security, Pervasive Computing


Automotive The U.S. Congress passed an infrastructure bill that includes mandates for the U.S. automobiles to install technology in new vehicles that will stop impaired drivers from driving a vehicle. Sec. 24220, the advanced impaired driving technology section of the bill says the Secretary of Transportation is responsible for coming up with standards after which the auto industry has at the ... » read more

Improving Power Efficiency In Ultra-Low Power Designs


Faster data communications in phones and data centers grabs headlines, but many applications don't require the continuous, high-data-rate communications needed for video streaming or image processing. In fact, for many devices, designing for better performance results in wasted energy and sharply curtails the time between battery charges. That is especially true for machine-to-machine (M2M) ... » read more

Getting Better Edge Performance & Efficiency From Acceleration-Aware ML Model Design


The advent of machine learning techniques has benefited greatly from the use of acceleration technology such as GPUs, TPUs and FPGAs. Indeed, without the use of acceleration technology, it’s likely that machine learning would have remained in the province of academia and not had the impact that it is having in our world today. Clearly, machine learning has become an important tool for solving... » read more

← Older posts