AI Adoption Slow For Design Tools

By Brian Bailey - 01 May, 2023 - Comments: 0

A lot of excitement, and a fair amount of hype, surrounds what artificial intelligence (AI) can do for the EDA industry. But many challenges must be overcome before AI can start designing, verifying, and implementing chips for us. Should AI replace the algorithms in use today, or does it have a different role to play? At the end of the day, AI is a technique that has strengths and weaknesses... » read more

Simulating Reality: The Importance Of Synthetic Data In AI/ML Systems For Radar Applications

By Arien Sligar - 09 Feb, 2023 - Comments: 0

Artificial intelligence and machine learning (AI/ML) are driving the development of next-generation radar perception. However, these AI/ML-based perception models require enough data to learn patterns and relationships to make accurate predictions on new, unseen data and scenarios. In the field of radar applications, the data used to train these models is often collected from real-world meas... » read more

Lithography Modeling: Data Augmentation Framework

By Technical Paper Link - 17 Nov, 2022 - Comments: 0

A new technical paper titled "An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design" was published by researchers at the University of Texas at Austin, Nvidia, and the California Institute of Technology. Abstract: "Lithography modeling is a crucial problem in chip design to ensure a chip design mask is manufacturable. It requires rigorous simulation... » read more

Training a Quantum Neural Network Requires Only A Small Amount of Data

By Technical Paper Link - 24 Aug, 2022 - Comments: 0

A new research paper titled "Generalization in quantum machine learning from few training data" was published by researchers at Technical University of Munich, Munich Center for Quantum Science and Technology (MCQST), Caltech, and Los Alamos National Lab. “Many people believe that quantum machine learning will require a lot of data. We have rigorously shown that for many relevant problems,... » read more

Safe And Robust Machine Learning

By Ed Sperling - 15 Jul, 2021 - Comments: 0

Deploying machine learning in the real world is a lot different than developing and testing it in a lab. Quenton Hall, AI systems architect at Xilinx, examines security implications on both the inferencing and training side, the potential for disruptions to accuracy, and how accessible these models and algorithms will be when they are used at the edge and in the cloud. This involves everything ... » read more

Things That Go Bump In The Daytime

By Ed Sperling - 14 Jan, 2020 - Comments: 0

There is no argument that autonomous technology is better at certain things than systems controlled by people. A computer-guided system has only one mission — to stay on the road, avoid object, and reach the end destination. It doesn't get tired, text, or look out the window. And it can park within a millimeter of a wall or another vehicle without hitting it, and do that every time — as lon... » read more

Leveraging Data In Chipmaking

By Ed Sperling - 12 Nov, 2019 - Comments: 0

John Kibarian, president and CEO of PDF Solutions, sat down with Semiconductor Engineering to talk about the impact of data analytics on everything from yield and reliability to the inner structure of organizations, how the cloud and edge will work together, and where the big threats are in the future. SE: When did you recognize that data would be so critical to hardware design and manufact... » read more

Making Sense Of ML Metrics

By Ed Sperling - 16 Oct, 2019 - Comments: 0

Steve Roddy, vice president of products for Arm’s Machine Learning Group, talks with Semiconductor Engineering about what different metrics actually mean, and why they can vary by individual applications and use cases. » read more

Holes In AI Security

By Ed Sperling - 06 Jun, 2019 - Comments: 0

Mike Borza, principal security technologist in Synopsys’ Solutions Group, explains why security is lacking in AI, why AI is especially susceptible to Trojans, and why small changes in training data can have big impacts on many devices. » read more

AI’s Growing Impact On Chip Design

By Ed Sperling - 16 Jan, 2019 - Comments: 0

Synopsys chairman and co-CEO Aart de Geus sat down with Semiconductor Engineering to talk about the rapid infusion of AI into electronics, how that is changing chip design and the software that runs on those chips. What follows are excerpts of that conversation. SE: We're dealing with a bunch of new markets, more customized design, and AI seems to be creeping into everything. How does this i... » read more

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Knowledge Centers
Entities, people and technologies explored

Shift Left Is The Tip Of The Iceberg

A transformative change is underway for semiconductor design and EDA. New languages, models, and abstractions will need to be created.

by Brian Bailey

Partitioning In The Chiplet Era

Understanding how chiplets interact under different workloads is critical to ensuring signal integrity and optimal performance in heterogeneous designs.

by Ann Mutschler

NAND Flash Targets 1,000 Layers

New techniques go beyond improved deposition and etching, but challenges stack up, too.

by Bryon Moyer

3.5D: The Great Compromise

Pros and cons of a middle-ground chiplet assembly that combines 2.5D and 3D-IC.

by Ed Sperling

AI’s Role In Chip Design Widens, Drawing In New Startups

Focus is on letting engineers do much more with the same or fewer resources — and less drudgery.

by Karen Heyman

What Comes After HBM For Chiplets

The standard for high-bandwidth memory limits design freedom at many levels, but that is required for interoperability. What freedoms can be taken from other functions to make chiplets possible?

by Brian Bailey

Memory Fundamentals For Engineers

eBook: Nearly everything you need to know about memory, including detailed explanations of the different types of memory; how and where these are used today; what's changing, which memories are successful and which ones might be in the future; and the limitations of each memory type.

by The SE Staff

New AI Processors Architectures Balance Speed With Efficiency

Hot Chips 24: Large language models ratchet up pressure for sustainable computing and heterogeneous integration; data management becomes key differentiator.

by Ed Sperling

tag: training data