tag: machine learning

Machine Learning — Everywhere: Enabling Self-Optimizing Design Platforms
By Synopsys - 29 Jul, 2020 - Comments: 0

Machine-learning offers opportunities to enable self-optimizing design tools. Very much like self-driving cars that observe real-world interactions to improve their responses in different (local) driving conditions, AI-enhanced tools are able to learn and improve in (local) design environments after deployment. These new, ML-driven capabilities can be embedded in different design engines, gi... » read more

Speeding Up The R&D Metrology Process
By Mark LaPedus - 16 Jul, 2020 - Comments: 0

Several chipmakers are making some major changes in the characterization/metrology lab, adding more fab-like processes in this group to help speed up chip development times. The characterization/metrology lab, which is generally under the radar, is a group that works with the R&D organization and the fab. The characterization lab is involved in the early analytical work for next-generati... » read more

It’s Eternal Spring For AI
By Rama Chellappa - 14 Jul, 2020 - Comments: 0

The field of Artificial Intelligence (AI) has had many ups and downs largely due to unrealistic expectations created by everyone involved including researchers, sponsors, developers, and even consumers. The “reemergence” of AI has lot to do with recent developments in supporting technologies and fields such as sensors, computing at macro and micro scales, communication networks and progre... » read more

Designer And IP Tracks Swell With Focus On ML, Security And Traditional EDA Methodologies
By Michelle Clancy Fuller - 13 Jul, 2020 - Comments: 0

What are designers keenly interested in as the 57th Design Automation Conference (DAC) approaches? If you said machine learning (ML), you’d be only partially right. Based on designer and IP tracks submissions to the 57th edition of the venerable electronics-industry event, ML – how to design with it and optimize EDA tools and flows using it – is a hot topic. But so too are more traditi... » read more

Machine Learning Enabled Root Cause Analysis For Low Power Verification
By Himanshu Bhatt - 13 Jul, 2020 - Comments: 0

By Himanshu Bhatt and Susantha Wijesekara Next-generation SoCs with advanced graphics, computing and artificial intelligence capabilities are posing unforeseen challenges in verification. Designers and verification engineers using static verification technologies for low power often see many violations in the initial stages. Efficient debugging and determining root cause is a real issue and ... » read more

Moving Data And Computing Closer Together
By Bryon Moyer - 09 Jul, 2020 - Comments: 2

The speed of processors has increased to the point where they often are no longer the performance bottleneck for many systems. It's now about data access. Moving data around costs both time and power, and developers are looking for ways to reduce the distances that data has to move. That means bringing data and memory nearer to each other. “Hard drives didn't have enough data flow to cr... » read more

Next Challenge: Known Good Systems
By Ed Sperling - 07 Jul, 2020 - Comments: 0

The leading edge of design is heading toward multi-die/multi-chiplet architectures, and an increasing number of mainstream designs likely will follow as processing moves closer to the edge. This doesn't mean every chipmaker will be designing leading-edge chips, of course. But more devices will have at least some leading-edge logic or will be connected over some advanced interconnect scheme t... » read more

Data Becomes Key For Next-Gen Chips
By SE Staff - 07 Jul, 2020 - Comments: 1

Data has become vital to understanding the useful life of a semiconductor — and the knowledge gleaned is key to staying competitive beyond Moore’s Law. What's changed is a growing reliance earlier in the design cycle on multiple sources of data, including some from further right in the design-through-manufacturing flow. While this holistic approach may seem logical enough, the semiconduc... » read more

Are Better Machine Training Approaches Ahead?
By Bryon Moyer - 01 Jul, 2020 - Comments: 1

We live in a time of unparalleled use of machine learning (ML), but it relies on one approach to training the models that are implemented in artificial neural networks (ANNs) — so named because they’re not neuromorphic. But other training approaches, some of which are more biomimetic than others, are being developed. The big question remains whether any of them will become commercially viab... » read more

Winners And Losers At The Edge
By Ed Sperling - 01 Jul, 2020 - Comments: 0

The edge is a vast collection of niches tied to narrow vertical markets, and it is likely to stay that way for years to come. This is both good and bad for semiconductor companies, depending upon where they sit in the ecosystem and their ability to adapt to a constantly shifting landscape. Some segments will see continued or new growth, including EDA, manufacturing equipment, IP, security an... » read more

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