I attend a lot of events on machine learning and write about it regularly. However, I learned some exciting new information about machine learning in a very surprising place recently. Every year for the last few years I have attended the HSPICE SIG dinner hosted by Synopsys in Santa Clara. This event starts with a vendor fair featuring… Read More
How could we differentiate between deep learning and machine learning as there are many ways of describing them? A simple definition of these software terms can be found here. Let’s look into Artificial Intelligence (AI), which was coined back in 1956. The term AI can be defined as human intelligence exhibited by machines.… Read More
If you haven’t noticed, there has been a BIG influx of money into Artificial Intelligence (AI) technologies. Most recently, the Chinese government announced that AI is one of their top initiatives with a goal to catch up with the United States within 3 years and to be the world leader in AI by the year 2030. Horizon Robotics, founded… Read More
I recently read a new white paper release by Mentor, a Siemens Business, that delved into the intricacies of Chemical Mechanical Polishing (CMP) and I got a sense of Déjà vu. My professional career in the IC industry started at Texas instruments and the white paper made me think of a conversation I had with one of my colleagues over … Read More
Neural networks make it possible to use machine learning for a wide variety of tasks, removing the need to write new code for each new task. Neural networks allow computers to use experiential learning instead of explicit programming to make decisions. The basic concepts related to neural networks were first proposed in the 1940’s,… Read More
TSMC held its Open Innovation Platform Forum the other week on September 13[SUP]th[/SUP]. Each year the companies that exhibit at this event choose to highlight their latest technology. One of the most interesting presentations that I received during the event was from Solido. In recent years they have produced a number of groundbreaking… Read More
Traditionally, EDA has been a brute force methodology where we buy more software licenses and more CPUs and keep running endless jobs to keep up with the increasing design and process complexities. SPICE simulation for example; when I meet chip designers (which I do quite frequently) I ask them how many simulations they do for a … Read More
Machine learning (ML) is the hot new technology of our time so EDA development teams are eagerly searching for new ways to optimize various facets of design using ML to distill wisdom from the mountains of data generated in previous designs. Pre-ML, we had little interest in historical data and would mostly look only at localized… Read More
At DAC this year you could learn a lot about hardware design for AI or Machine Learning (ML) applications. We are all familiar with the massively parallel hardware being developed for autonomous vehicles, cloud computing, search engines and the like. This includes, for instance, hardware from Nvidia and others that enable ML … Read More
Verification as an effectively unbounded problem will always stir debate on ways to improve. A natural response is to put heavy emphasis on making existing methods faster and more seamless. That’s certainly part of continuous improvement but sometimes we also need to step back and ask the bigger questions – what is sufficient … Read More
