tag: ML

Operator Anxiety
By Steve Roddy - 12 Jan, 2023 - Comments: 0

Are you one of the early pioneers who have purchased an electric car? In the United States in Q3 2022, 6% of new vehicle sales were pure electric models. Despite all the hype — and significant purchase subsidies in support of battery cars — today only 1% of the cumulative number of vehicles in service in the US are purely plug-in electric. One of the reasons electric car sales have not full... » read more

Variability Becoming More Problematic, More Diverse
By Karen Heyman - 12 Dec, 2022 - Comments: 0

Process variability is becoming more problematic as transistor density increases, both in planar chips and in heterogeneous advanced packages. On the basis of sheer numbers, there are many more things that can wrong. “If you have a chip with 50 billion transistors, then there are 50 places where a one-in-a-billion event can happen,” said Rob Aitken, a Synopsys fellow. And if Intel’s... » read more

Don’t Let Your ML Accelerator Vendor Tell You The ‘F-Word’
By Steve Roddy - 08 Dec, 2022 - Comments: 0

Machine learning (ML) inference in devices is all the rage. Nearly every new system on chip (SoC) design start for mobile phones, tablets, smart security cameras, automotive applications, wireless systems, and more has a requirement for a hefty amount of ML capability on-chip. That has silicon design teams scrambling to find ML processing power to add to the existing menu of processing engines ... » read more

Multiexpert Adversarial Regularization For Robust And Data-Efficient Deep Supervised Learning
By Samsung - 16 Nov, 2022 - Comments: 0

Deep neural networks (DNNs) can achieve high accuracy when there is abundant training data that has the same distribution as the test data. In practical applications, data deficiency is often a concern. For classification tasks, the lack of enough labeled images in the training set often results in overfitting. Another issue is the mismatch between the training and the test domains, which resul... » read more

Improving Reliability In Automobiles
By John Koon - 14 Nov, 2022 - Comments: 0

Carmakers are turning to predictive and preventive maintenance to improve the safety and reliability of increasingly electrified vehicles, setting the stage for more internal and external sensors, and more intelligence to interpret and react to the data generated by those sensors. The number of chips inside of vehicles has been steadily rising, regardless of whether they are powered by elect... » read more

GDDR6 Memory Enables High-Performance AI/ML Inference
By Frank Ferro - 10 Nov, 2022 - Comments: 0

A rapid rise in the size and sophistication of inference models has necessitated increasingly powerful hardware deployed at the network edge and in endpoint devices. To keep these inference processors and accelerators fed with data requires a state-of-the-art memory that delivers extremely high bandwidth. This blog will explore how GDDR6 supports the memory and performance requirements of artif... » read more

Securing Accelerator Blades For Datacenter AI/ML Workloads
By Emma-Jane Crozier - 25 Oct, 2022 - Comments: 0

Data centers handle huge amounts of AI/ML training and inference workloads for their individual customers. Such a vast number of workloads calls for efficient processing, and to handle these workloads we have seen many new solutions emerge in the market. One of these solutions is pluggable accelerator blades, often deployed in massively parallel arrays, that implement the latest state-of-the-ar... » read more

Auto Safety Tech Adds New IC Design Challenges
By John Koon - 06 Oct, 2022 - Comments: 1

The role of AI/ML in automobiles is widening as chipmakers incorporate more intelligence into chips used in vehicles, setting the stage for much safer vehicles, fewer accidents, but much more complex electronic systems. While full autonomy is still on the distant horizon, the short-term focus involves making sure drivers are aware of what's going on around them — pedestrians, objects, or o... » read more

Deep Learning To Classify And Establish Structure Property Predictions With PeakForce QNM Atomic Force Microscopy
By Bruker - 07 Sep, 2022 - Comments: 0

Machine learning and specifically, deep learning, is a powerful tool to establish the presence (or absence) of microstructure correlations to bulk properties with its ability to flesh out relationships and trends that are difficult to establish otherwise. This application note discusses the use of deep learning tools, to explore AFM phase and PeakForce Quantitative Nanomechanics (QNM) im... » read more

Challenges Mount In New Autos
By John Koon - 06 Sep, 2022 - Comments: 0

Electronics are becoming the primary differentiator for carmakers, adding an array of options that can alter everything from how a vehicle's occupants interact with their surroundings to how the vehicle drives. But the infrastructure needed to support these features also raises a slew of technology and business questions for which there are no simple answers today. For example, how will new ... » read more

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