Greener Design Verification


Chip designs are optimized for lower cost, better performance, or lower power. The same cannot be said about verification, where today very little effort is spent on reducing execution cost, run time, or power consumption. Admittedly, one is a per unit cost while the other is a development cost, but could the industry be doing more to make development greener? It can take days for regression... » read more

Next Generation Reservoir Computing


Abstract: "Reservoir computing is a best-in-class machine learning algorithm for processing information generated by dynamical systems using observed time-series data. Importantly, it requires very small training data sets, uses linear optimization, and thus requires minimal computing resources. However, the algorithm uses randomly sampled matrices to define the underlying recurrent neural n... » read more

Is Programmable Overhead Worth The Cost?


Programmability has fueled the growth of most semiconductor products, but how much does it actually cost? And is that cost worth it? The answer is more complicated than a simple efficiency formula. It can vary by application, by maturity of technology in a particular market, and in the context of much larger systems. What's considered important for one design may be very different for anothe... » read more

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

Amdahl Limits On AI


Software and hardware both place limits on how fast an application can run, but finding and eliminating the limitations is becoming more important in this age of multicore heterogeneous processing. The problem is certainly not new. Gene Amdahl (1922-2015) recognized the issue and published a paper about it in 1967. It provided the theoretical speedup for a defined task that could be expected... » read more

The Future Of Smart Cameras Is 64-Bit Processing


The future of smart camera technology brings with it profound transformations in the way we interact with each other and the world around us. From smart cities that are safer and more efficient to rainforests that are monitored for illegal logging, the increasing need for advanced vision technology is growing. Diverse and complex use cases leveraging artificial intelligence (AI) and machine lea... » read more

Deep Learning Delivers Fast, Accurate Solutions For Object Detection In The Automated Optical Inspection Of Electronic Assemblies


When automated optical inspection (AOI) works, it is almost always preferable to human visual inspection. It can be faster, more accurate, more consistent, less expensive, and it never gets tired. However, some tasks that are very simple for humans are quite difficult for machines. Object detection is an example. For example, shown an image containing a cat, a dog, and a duck, a human can insta... » 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

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