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

HBM2E Raises The Bar For Memory Bandwidth


AI/ML training capabilities are growing at a rate of 10X per year driving rapid improvements in every aspect of computing hardware and software. HBM2E memory is the ideal solution for the high bandwidth requirements of AI/ML training, but entails additional design considerations given its 2.5D architecture. Designers can realize the full benefits of HBM2E memory with the silicon-proven memory s... » read more

Enablers And Barriers For Connecting Diverse Data


More data is being collected at every step of the manufacturing process, raising the possibility of combining data in new ways to solve engineering problems. But this is far from simple, and combining results is not always possible. The semiconductor industry’s thirst for data has created oceans of it from the manufacturing process. In addition, semiconductor designs large and small now ha... » read more

Solving Real World AI Productization Challenges With Adaptive Computing


The field of artificial intelligence (AI) moves swiftly, with the pace of innovation only accelerating. While the software industry has been successful in deploying AI in production, the hardware industry – including automotive, industrial, and smart retail – is still in its infancy in terms of AI productization. Major gaps still exist that hinder AI algorithm proof-of-concepts (PoC) from b... » 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

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