100G Ethernet IP For Edge Computing


The presence of Ethernet in our lives has paved the way for the emergence of the Internet of Things (IoT). Ethernet has connected everything around us and beyond, from smart homes and businesses, to industries, schools, and governments. This specification is even found in our vehicles, facilitating communication between internal devices. Ethernet has enabled high-performance computing data cent... » read more

Designing for Data Flow


Movement and management of data inside and outside of chips is becoming a central theme for a growing number of electronic systems, and a huge challenge for all of them. Entirely new architectures and techniques are being developed to reduce the movement of data and to accomplish more per compute cycle, and to speed the transfer of data between various components on a chip and between chips ... » read more

Looking Inside Of Chips


Shai Cohen, co-founder and CEO of proteanTecs, sat down with Semiconductor Engineering to talk about how to boost reliability and add resiliency into chips and advanced packaging. What follows are excerpts of that conversation. SE: Several years ago, no one was thinking about on-chip monitoring. What's changed? Cohen: Today it is obvious that a solution is needed for optimizing performanc... » read more

Improving Chip Efficiency, Reliability, And Adaptability


Peter Schneider, director of Fraunhofer Institute for Integrated Circuits' Engineering of Adaptive Systems Division, sat down with Semiconductor Engineering to talk about new models and approaches for ensuring the integrity and responsiveness of systems, and how this can be done within a given power budget and at various speeds. What follows are excerpts of that conversation. SE: Where are y... » read more

AI At The IoT Edge Is Disrupting The Industrial Market


Artificial intelligence (AI) at the edge of the network is a cornerstone that will influence the future direction of the technology industry. If AI is an engine of change, then semiconductors are the oil driving the new age that is being defined by machine learning (ML), neural networks, 5G connectivity and the advent of blockchain, digital twins and the metaverse. Despite recent disruptions... » read more

MIPI In Next Generation Of AI IoT Devices At The Edge


The history of data processing begins in the 1960’s with centralized on-site mainframes that later evolved into distributed client servers. In the beginning of this century, centralized cloud computing became attractive and began to gain momentum becoming one of the most popular computing tools today. In recent years however, we have seen an increase in the demand for processing... » read more

AI At The Edge: Optimizing AI Algorithms Without Sacrificing Accuracy


The ultimate measure of success for AI will be how much it increases productivity in our daily lives. However, the industry has huge challenges in evaluating progress. The vast number of AI applications is in constant churn: finding the right algorithm, optimizing the algorithm, and finding the right tools. In addition, complex hardware engineering is rapidly being updated with many different s... » read more

Repositioning For A Changing IC Market


Sailesh Chittipeddi, executive vice president at Renesas, sat down with Semiconductor Engineering to talk about how changes in end markets are shifting demand for technology. What follows are excerpts of that conversation. SE: Renesas has acquired a number of companies over the past several years. What's the goal? Chittipeddi: The goal very simply is to create an industry leading solutio... » read more

Machine Learning Showing Up As Silicon IP


New machine-learning (ML) architectures continue to appear. Up to now, each new offering has been implemented in a chip for sale, to be placed alongside host processors, memory, and other chips on an accelerator board. But over time, more of this technology could be sold as IP that can be integrated into a system-on-chip (SoC). That trend is evident at recent conferences, where an increasing... » read more

ML Focus Shifting Toward Software


New machine-learning (ML) architectures continue to garner a huge amount of attention as the race continues to provide the most effective acceleration architectures for the cloud and the edge, but attention is starting to shift from the hardware to the software tools. The big question now is whether a software abstraction eventually will win out over hardware details in determining who the f... » read more

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