tag: machine learning

Have Processor Counts Stalled?
By Brian Bailey - 24 Sep, 2020 - Comments: 2

Survey data suggests that additional microprocessor cores are not being added into SoCs, but you have to dig into the numbers to find out what is really going on. The reasons are complicated. They include everything from software programming models to market shifts and new use cases. So while the survey numbers appear to be flat, market and technology dynamics could have a big impact in resh... » read more

New Uses For Assertions
By Ann Mutschler - 24 Sep, 2020 - Comments: 0

Assertions have been a staple in formal verification for years. Now they are being examined to see what else they can be used for, and the list is growing. Traditionally, design and verification engineers have used assertions in specific ways. First, there are assertions for formal verification, which are used by designers to show when something is wrong. Those assertions help to pinpoint wh... » read more

Productivity Keeping Pace With Complexity
By Brian Bailey - 24 Sep, 2020 - Comments: 0

Designs have become larger and more complex and yet design time has shortened, but team sizes remain essentially flat. Does this show that productivity is keeping pace with complexity for everyone? The answer appears to be yes, at least for now, for a multitude of reasons. More design and IP reuse is using more and larger IP blocks and subsystems. In addition, the tools are improving, and mo... » read more

Neural Networks Without Matrix Math
By Katherine Derbyshire - 17 Sep, 2020 - Comments: 0

The challenge of speeding up AI systems typically means adding more processing elements and pruning the algorithms, but those approaches aren't the only path forward. Almost all commercial machine learning applications depend on artificial neural networks, which are trained using large datasets with a back-propagation algorithm. The network first analyzes a training example, typically assign... » read more

How ML Enables Cadence Digital Tools To Deliver Better PPA
By Cadence - 09 Sep, 2020 - Comments: 0

Artificial intelligence (AI) and machine learning (ML) are emerging as powerful new ways to do old things more efficiently, which is the benchmark that any new and potentially disruptive technology must meet. In chip design, results are measured in many different ways, but common metrics are power (consumed), performance (provided), and area (required), collectively referred to as PPA. These me... » read more

From Data Center To End Device: AI/ML Inferencing With GDDR6
By Rambus - 09 Sep, 2020 - Comments: 0

Created to support 3D gaming on consoles and PCs, GDDR packs performance that makes it an ideal solution for AI/ML inferencing. As inferencing migrates from the heart of the data center to the network edge, and ultimately to a broad range of AI-powered IoT devices, GDDR memory’s combination of high bandwidth, low latency, power efficiency and suitability for high-volume applications will be i... » read more

Challenges In Using AI In Verification
By Ann Mutschler - 27 Aug, 2020 - Comments: 0

Pressure to use AI/ML techniques in design and verification is growing as the amount of data generated from complex chips continues to explode, but how to begin building those capabilities into tools, flows and methodologies isn't always obvious. For starters, there is debate about whether the data needs to be better understood before those techniques are used, or whether it's best to figure... » read more

Artificial Intelligence And Machine Learning Add New Capabilities to Traditional RF EDA Tools
By Cadence - 26 Aug, 2020 - Comments: 0

This article features contributions from RF EDA vendors on their various capabilities for artificial intelligence and machine learning. AWR Design Environment software is featured and highlights the network synthesis wizard. Click here to continue reading. » read more

Finding Defects With E-Beam Inspection
By Mark LaPedus - 20 Aug, 2020 - Comments: 0

Several companies are developing or shipping next-generation e-beam inspection systems in an effort to reduce defects in advanced logic and memory chips. Vendors are taking two approaches with these new e-beam inspection systems. One is a more traditional approach, which uses a single-beam e-beam system. Others, meanwhile, are developing newer multi-beam technology. Both approaches have thei... » read more

Scaling AI/ML Training Performance With HBM2E Memory
By Frank Ferro - 13 Aug, 2020 - Comments: 0

In my April SemiEngineering Low Power-High Performance blog, I wrote: “Today, AI/ML neural network training models can exceed 10 billion parameters, soon it will be over 100 billion.” “Soon” didn’t take long to arrive. At the end of May, OpenAI unveiled a new 175-billion parameter GPT-3 language model. This represented a more that 100X jump over the size of GPT-2’s 1.5 billion param... » read more

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