A few months ago, I wrote about the announcement of a new digital full flow from Cadence. In that piece, I focused on the machine learning (ML) aspects of the new tool. I had covered a discussion with Cadence’s Paul Cunningham a week before that explored ML in Cadence products, so it was timely to dive into a real-world example of the … Read More
There have been a multitude of announcements recently relative to the incorporation of machine learning (ML) methods into EDA tool algorithms, mostly in the physical implementation flows. For example, deterministic ML-based decision algorithms applied to cell placement and signal interconnect routing promise to expedite… Read More
Webinars are chosen during registration
Digital Implementation Flow Automation and Vivid Design Metrics Visualisation
June 10, 2020; 15:00 (UKT) 16:00 (CEST) 17:00 (EEST/IDT)
Speaker: Benoir Carpentier
Creating a final design is a sequence of operations from RTL synthesis, through implementation to sign-off.
If you are working on complex Artificial Intelligence (AI) or Machine Learning (ML) or Deep Learning (DL) designs using advanced node processes, you will understand the motivations for optimising CPU utilisation, device power and processing speed. Cutting-edge AI, ML & DL chips, by their very nature, are susceptible to… Read More
One of the side benefits of working with SemiWiki is that you get to meet a broad range of people and in the semiconductor industry that means a broad range of very smart people, absolutely.
Recently I had the pleasure to meet Richard McPartland of Moortec. Richard and I started in the semiconductor industry at the same time but from… Read More
Machine Learning (ML) has become extremely important for many computing applications, especially ones that involve interacting with the physical world. Along with this trend has come the development of many specialized ML processors for cloud and mobile applications. These chips work fine in the cloud or even in cars or phones,… Read More
The use of machine learning (ML) to solve complex problems that could not previously be addressed by traditional computing is expanding at an accelerating rate. Even with advances in neural network design, ML’s efficiency and accuracy are highly dependent on the training process. The methods used for training evolved from CPU… Read More
I already posted on one automotive panel at this year’s Arm TechCon. A second I attended was a more open-ended discussion on where we’re really at in autonomous driving. Most of you probably agree we’ve passed the peak of the hype curve and are now into the long slog of trying to connect hope to reality. There are a lot of challenges, … Read More
The market opportunities for machine learning hardware are becoming more succinct, with the following (rather broad) categories emerging:
- Model training: models are evaluated at the “hyperscale” data center; utilizing either general purpose processors or specialized hardware, with typical numeric precision of 32-bit
We have learned from nature that two characteristics are helpful for success, diversity and adaptability. The same has been shown to be true for computing systems. Things have come a long way from when CPU centric computing was the only choice. Much heavy lifting these days is done by GPUs, ASICs, and FPGAs, with CPUs in a support … Read More