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More Data Drives Focus On IC Energy Efficiency


Computing workloads are becoming increasingly interdependent, raising the complexity level for chip architects as they work out exactly where that computing should be done and how to optimize it for shrinking energy margins. At a fundamental level, there is now more data to compute and more urgency in getting results. This situation has forced a rethinking of how much data should be moved, w... » read more

AI In Inspection, Metrology, And Test


AI/ML is creeping into multiple processes within the fab and packaging houses, although not necessarily for the purpose it was originally intended. The chip industry is just beginning to learn where AI makes sense and where it doesn't. In general, AI works best as a tool in the hands of someone with deep domain expertise. AI can do certain things well, particularly when it comes to pattern m... » read more

How Do Machines Learn?


We depend, or hope to depend, on machines, especially computers, to do many things, from organizing our photos to parking our cars. Machines are becoming less and less "mechanical" and more and more "intelligent." Machine learning has become a familiar phrase to many people in advanced manufacturing. The next natural question people may ask is: How do machines learn? Recognizing diverse obje... » read more

New Uses For AI


AI is being embedded into an increasing number of technologies that are commonly found inside most chips, and initial results show dramatic improvements in both power and performance. Unlike high-profile AI implementations, such as self-driving cars or natural language processing, much of this work flies well under the radar for most people. It generally takes the path of least disruption, b... » read more

How To Measure ML Model Accuracy


Machine learning (ML) is about making predictions about new data based on old data. The quality of any machine-learning algorithm is ultimately determined by the quality of those predictions. However, there is no one universal way to measure that quality across all ML applications, and that has broad implications for the value and usefulness of machine learning. “Every industry, every d... » read more

Xilinx AI Engines And Their Applications


This white paper explores the architecture, applications, and benefits of using Xilinx's new AI Engine for compute intensive applications like 5G cellular and machine learning DNN/CNN. 5G requires between five to 10 times higher compute density when compared with prior generations; AI Engines have been optimized for DSP, meeting both the throughput and compute requirements to deliver the hig... » read more

Tradeoffs To Improve Performance, Lower Power


Generic chips are no longer acceptable in competitive markets, and the trend is growing as designs become increasingly heterogeneous and targeted to specific workloads and applications. From the edge to the cloud, including everything from vehicles, smartphones, to commercial and industrial machinery, the trend increasingly is on maximizing performance using the least amount of energy. This ... » read more

HBM2E Raises The Bar For AI/ML Training


The largest AI/ML neural network training models now exceed an enormous 100 billion parameters. With the rate of growth over the last decade on a 10X annual pace, we’re headed to trillion parameter models in the not-too-distant future. Given the tremendous value that can be derived from AI/ML (it is mission critical to five of six of the top market cap companies in the world), there has been ... » read more

Firmware Skills Shortage


Good hardware without good software is a waste of silicon, but with so many new processors and accelerator architectures being created, and so many new skills required, companies are finding it hard to hire enough engineers with low-level software expertise to satisfy the demand. Writing compilers, mappers and optimization software does not have the same level of pizazz as developing new AI ... » read more

Part Average Tests For Auto ICs Not Good Enough


Part Average Testing (PAT) has long been used in automotive. For some semiconductor technologies it remains viable, while for others it is no longer good enough. Automakers are bracing for chips developed at advanced process nodes with much trepidation. Tight control of their supply chains and a reliance upon mature electronic processes so far have enabled them to increase electronic compone... » read more

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