tag: machine learning

Do We Have An IC Model Crisis?
By Brian Bailey - 27 May, 2021 - Comments: 0

Models are critical for IC design. Without them, it's impossible to perform analysis, which in turn limits optimizations. Those optimizations are especially important as semiconductors become more heterogenous, more customized, and as they are integrated into larger systems, creating a need for higher-accuracy models that require massive compute power to develop. But those factors, and other... » read more

Scaling Simulation
By Brian Bailey - 27 May, 2021 - Comments: 1

Without functional simulation the semiconductor industry would not be where it is today, but some people in the industry contend it hasn't received the attention and research it deserves, causing a stagnation in performance. Others disagree, noting that design sizes have increased by orders of magnitude while design times have shrunk, pointing to simulation remaining a suitable tool for the job... » read more

One-On-One: Lip-Bu Tan
By Ed Sperling - 26 May, 2021 - Comments: 0

Lip-Bu Tan, CEO of Cadence, sat down with Semiconductor Engineering to talk about the impact of massive increases in data across a variety of industries, the growing need for computational software, and the potential implications of U.S.-China relations. What follows are excerpts of that discussion. SE: What do you see as the biggest change for the chip industry? Tan: We're in our fifth g... » read more

The Increasingly Uneven Race To 3nm/2nm
By Mark LaPedus and Ed Sperling - 24 May, 2021 - Comments: 2

Several chipmakers and fabless design houses are racing against each other to develop processes and chips at the next logic nodes in 3nm and 2nm, but putting these technologies into mass production is proving both expensive and difficult. It's also beginning to raise questions about just how quickly those new nodes will be needed and why. Migrating to the next nodes does boost performance an... » read more

Machine Learning At The Edge
By Siemens EDA - 12 May, 2021 - Comments: 0

Moving machine learning to the edge has critical requirements on power and performance. Using off-the-shelf solutions is not practical. CPUs are too slow, GPUs/TPUs are expensive and consume too much power, and even generic machine learning accelerators can be overbuilt and are not optimal for power. In this paper, learn about creating new power/memory efficient hardware architectures to meet n... » read more

Kria K26 SOM: The Ideal Platform For Vision AI At The Edge
By Xilinx - 12 May, 2021 - Comments: 0

With various advancements in artificial intelligence (AI) and machine learning (ML) algorithms, many high-compute applications are now getting deployed on edge devices. So, there is a need for an efficient hardware that can execute complex algorithms efficiently as well as adapt to rapid enhancements in this technology. Xilinx's Kria K26 SOM is designed to address the requirements of executing ... » read more

Developers Turn To Analog For Neural Nets
By Bryon Moyer - 06 May, 2021 - Comments: 1

Machine-learning (ML) solutions are proliferating across a wide variety of industries, but the overwhelming majority of the commercial implementations still rely on digital logic for their solution. With the exception of in-memory computing, analog solutions mostly have been restricted to universities and attempts at neuromorphic computing. However, that’s starting to change. “Everyon... » read more

Securing AI/ML With A Hardware Root Of Trust
By Thierry Kouthon - 06 May, 2021 - Comments: 0

AI/ML (Artificial Intelligence/Machine Learning) is now pervasive across all industries. It contributes to rationalizing and harnessing the enormous amount of information made available by the current massive wave of digitization. Digitization is transforming how business is run and how value is produced using digital technologies. Data, the raw material of AI/ML and deep learning algorithms, i... » read more

Steep Spike For Chip Complexity And Unknowns
By Ed Sperling - 05 May, 2021 - Comments: 0

Cramming more and different kinds of processors and memories onto a die or into a package is causing the number of unknowns and the complexity of those designs to skyrocket. There are good reasons for combining all of these different devices into an SoC or advanced package. They increase functionality and can offer big improvements in performance and power that are no longer available just b... » read more

Roadblocks For ML in EDA
By Brian Bailey - 28 Apr, 2021 - Comments: 1

Is EDA a suitable space for utilizing machine learning (ML)? The answer depends on a number of factors, including where exactly it is being applied, how much support there is from the industry, and whether there are demonstrable advantages. Exactly where ML will play a role has yet to be decided. Replacing existing heuristics with machine learning, for example, would require an industry-wide... » read more

← Older posts Newer posts →

  • Sponsors

About

Navigation

Connect With Us

Copyright ©2013-2025 SMG   |  Terms of Service  |  Privacy Policy