tag: ML

Nightmare Fuel: The Hazards Of ML Hardware Accelerators
By Steve Roddy - 13 Apr, 2023 - Comments: 0

A major design challenge facing numerous silicon design teams in 2023 is building the right amount of machine learning (ML) performance capability into today’s silicon tape out in anticipation of what the state of the art (SOTA) ML inference models will look like in 2026 and beyond when that silicon will be used in devices in volume production. Given the continuing rapid rate of change in mac... » read more

Getting Smarter About Tool Maintenance
By Anne Meixner - 16 Mar, 2023 - Comments: 0

Chipmakers have begun to shift to predictive maintenance for process tools, but the hefty investment in analytics and engineering efforts means it will take some time for smart maintenance to become a widespread practice. Semiconductor manufacturers need to maintain a diverse set of equipment to process the flow of wafers, dies, packaged parts, and boards running through factories. OSAT and ... » read more

AI Benchmarks Are Broken
By Jon Bunting - 14 Mar, 2023 - Comments: 0

Artificial Intelligence (AI) is shaping up to be one of the most revolutionary technologies of our time. By now you’ve probably heard that AI’s impact will transform entire industries, from healthcare to finance to entertainment, delivering us richer products, streamlined experiences, and augment human productivity, creativity, and leisure. Even non-technologists are getting a glimpse of... » read more

A New Year’s Wish
By Brian Bailey - 26 Jan, 2023 - Comments: 0

Every year I run a predictions article. It is a mashup of ideas from many people within the industry, and while many predictions are somewhat self-serving, there are other which come more from the heart — or perhaps they are dreams rather than expectations. I see hope in some of those, particularly the ones that look toward sustainability within our industry, and of our industry. Just like... » read more

What Does 2023 Have In Store For Chip Design?
By Brian Bailey - 26 Jan, 2023 - Comments: 0

Predictions seem to be easier to make during times of stability, but they are no more correct than at any other period. During more turbulent times, fewer people are courageous enough to allow their opinions to be heard. And yet it is often those views that are more well thought through, and even if they turn out not to be true, they often contain some very enlightening ideas. 2022 saw some ... » read more

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

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