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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

Reliability Concerns Shift Left Into Chip Design


Demand for lower defect rates and higher yields is increasing, in part because chips are now being used for safety- and mission-critical applications, and in part because it's a way of offsetting rising design and manufacturing costs. What's changed is the new emphasis on solving these problems in the initial design. In the past, defectivity and yield were considered problems for the fab. Re... » read more

Flexible USB4-Based Interface IP Solution For AI At The Edge


Consumers have become accustomed to smart devices that are powered by advances in artificial intelligence (AI). To expand the devices’ total addressable market, innovative device designers build edge AI accelerators and edge AI SoCs that support multiple use cases and integration options. This white paper describes a flexible USB4-based IP solution for edge AI accelerators and SoCs. The IP so... » read more

Seeking Scale, Semiconductor Companies Embrace IoT Framework


Fragmentation has long been the IoT’s greatest impediment. Even before the "Internet of Things" entered popular lexicon, infinite opportunity had turned into infinite complexity as companies raced to deliver solutions without any common technological framework or set of standards that might have ensured that software could be ported between technologies or hardware platforms. To bring orde... » read more

Easier And Faster Ways To Train AI


Training an AI model takes an extraordinary amount of effort and data. Leveraging existing training can save time and money, accelerating the release of new products that use the model. But there are a few ways this can be done, most notably through transfer and incremental learning, and each of them has its applications and tradeoffs. Transfer learning and incremental learning both take pre... » read more

Deploying Artificial Intelligence At The Edge


By Pushkar Apte and Tom Salmon Rapid advances in artificial intelligence (AI) have made this technology important for many industries, including finance, energy, healthcare, and microelectronics. AI is driving a multi-trillion-dollar global market while helping to solve some tough societal problems such as tracking the current pandemic and predicting the severity of climate-driven events lik... » read more

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