Bespoke Silicon Redefines Custom ASICs


Semiconductor Engineering sat down to discuss bespoke silicon and what's driving that customization with Kam Kittrell, vice president of product management in the Digital & Signoff group at Cadence; Rupert Baines, chief marketing officer at Codasip; Kevin McDermott, vice president of marketing at Imperas; Mo Faisal, CEO of Movellus; Ankur Gupta, vice president and general manager of Siemens... » read more

Distilling The Essence Of Four DAC Keynotes


Chip design and verification are facing a growing number of challenges. How they will be solved — particularly with the addition of machine learning — is a major question for the EDA industry, and it was a common theme among four keynote speakers at this month's Design Automation Conference. DAC has returned as a live event, and this year's keynotes involved the leaders of a systems comp... » read more

Week in Review: Design, Low Power


Acquisitions Renesas completed its acquisition of Reality Analytics, which specializes in embedded AI and TinyML solutions for advanced non-visual sensing in automotive, industrial and commercial products. Siemens Digital Industries Software will acquire Zona Technology, which develops aerospace simulation software. Siemens plans to integrate that software into its wXcelerator and Simcenter... » read more

Using AI To Speed Up Edge Computing


AI is being designed into a growing number of chips and systems at the edge, where it is being used to speed up the processing of massive amounts of data, and to reduce power by partitioning and prioritization. That, in turn, allows systems to act upon that data more rapidly. Processing data at the edge rather than in the cloud provides a number of well-documented benefits. Because the physi... » read more

Customization, Heterogenous Integration, And Brute Force Verification


Semiconductor Engineering sat down to discuss why new approaches are required for heterogeneous designs, with Bari Biswas, senior vice president for the Silicon Realization Group at Synopsys; John Lee, general manager and vice president of the Ansys Semiconductor business unit; Michael Jackson, corporate vice president for R&D at Cadence; Prashant Varshney, head of product for Microsoft Azu... » read more

What Future Processors Will Look Like


Mark Papermaster, CTO at AMD, sat down with Semiconductor Engineering to talk about architectural changes that are required as the benefits of scaling decrease, including chiplets, new standards for heterogeneous integration, and different types of memory. What follows are excerpts of that conversation. SE: What does a processor look like in five years? Is it a bunch of chips in a package? I... » read more

Improving Yield With Machine Learning


Machine learning is becoming increasingly valuable in semiconductor manufacturing, where it is being used to improve yield and throughput. This is especially important in process control, where data sets are noisy. Neural networks can identify patterns that exceed human capability, or perform classification faster. Consequently, they are being deployed across a variety of manufacturing proce... » read more

E-beam’s Role Grows For Detecting IC Defects


The perpetual march toward smaller features, coupled with growing demand for better reliability over longer chip lifetimes, has elevated inspection from a relatively obscure but necessary technology into one of the most critical tools in fab and packaging houses. For years, inspection had been framed as a battle between e-beam and optical microscopy. Increasingly, though, other types of insp... » read more

Which Fuel Will Drive Next-Generation Autos?


With gasoline prices hitting uncomfortable highs, consumers increasingly are looking toward non-gasoline-powered vehicles. But what ultimately will power those vehicles is far from clear. Inside the cabin and under the hood, these vehicles will be filled with semiconductors. Yet what the energy source is for those semiconductors is the subject of ongoing debate. It could be batteries, hydrog... » read more

AI At The Edge: Optimizing AI Algorithms Without Sacrificing Accuracy


The ultimate measure of success for AI will be how much it increases productivity in our daily lives. However, the industry has huge challenges in evaluating progress. The vast number of AI applications is in constant churn: finding the right algorithm, optimizing the algorithm, and finding the right tools. In addition, complex hardware engineering is rapidly being updated with many different s... » read more

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