Repositioning For A Changing IC Market


Sailesh Chittipeddi, executive vice president at Renesas, sat down with Semiconductor Engineering to talk about how changes in end markets are shifting demand for technology. What follows are excerpts of that conversation. SE: Renesas has acquired a number of companies over the past several years. What's the goal? Chittipeddi: The goal very simply is to create an industry leading solutio... » read more

Neuromorphic Chips & Power Demands


Research paper titled "A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware," from researchers at Graz University of Technology and Intel Labs. Abstract "Spike-based neuromorphic hardware holds the promise to provide more energy efficient implementations of Deep Neural Networks (DNNs) than standard hardware such as GPUs. But this requires to understand how D... » read more

Deep Learning In Industrial Inspection


Deep learning is at the upper end of AI complexity, sifting through more data to achieve more accurate results. Charlie Zhu, vice president of R&D at CyberOptics, talks about how DL can be utilized with inspection to identify defects in chips that are not discernible by traditional computer vision algorithms, classifying multiple objects simultaneously from multiple angles and taking into accou... » read more

Shortages Spark Novel Component Lifecycle Solutions


The semiconductor industry’s supply chain problems are prompting some innovative solutions and workarounds, and while they don't solve all problems, they are improving efficiency and extending equipment lifetimes. The shortages, which affect everything from the chips used in automotive, IoT, and consumer ICs to the equipment used to manufacture and test them — span global supply lines. T... » read more

Transforming AI Models For Accelerator Chips


AI is all about speeding up the movement and processing of data. Ali Cheraghi, solution architect at Flex Logix, talks about why floating point data needs to be converted into integer point data, how that impacts power and performance, and how different approaches in quantization play into this formula. » read more

Improving PPA With AI


AI/ML/DL is starting to show up in EDA tools for a variety of steps in the semiconductor design flow, many of them aimed at improving performance, reducing power, and speeding time to market by catching errors that humans might overlook. It's unlikely that complex SoCs, or heterogeneous integration in advanced packages, ever will be perfect at first silicon. Still, the number of common error... » read more

NVMe-oF: Simple, Invisible Fabric For Distributed Storage Networks


In today’s fast paced world, we need seamless access to huge chunks of data and new-world technologies, such as artificial intelligence (AI), machine learning (ML), cloud computing, and real-time data analytics. AI researchers are deriving applications such as cyber security analysis and intelligent virtual assistants (IVA) where the computer needs to process an intense amount of data. Theref... » read more

How AI/ML Improves Fab Operations


Chip shortages are forcing fabs and OSATs to maximize capacity and assess how much benefit AI and machine learning can provide. This is particularly important in light of the growth projections by market analysts. The chip manufacturing industry is expected to double in size over the next five years, and collective improvements in factories, AI databases, and tools will be essential for doub... » read more

Zero Dark Silicon


Planning for AI requires an understanding of how much data needs to be processed and how quickly that needs to happen. Nick Ni, senior director of data center AI and compute markets at AMD, talks with Semiconductor Engineering about data bubbles and domain-specific designs, why dark silicon is no longer as useful as in the past, and how to optimize power and performance in both the data center ... » read more

Novel H2H mapping algorithm with both computation and communication awareness


New research paper "H2H: Heterogeneous Model to Heterogeneous System Mapping with Computation and Communication Awareness" from University of Pittsburgh, Georgia Tech. Abstract: "The complex nature of real-world problems calls for heterogeneity in both machine learning (ML) models and hardware systems. The heterogeneity in ML models comes from multi-sensor perceiving and multi-task lear... » read more

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