Considering The Power Of The Cloud For EDA


By Michael White, Siemens EDA, in technical collaboration with Peeyush Tugnawat, Google Cloud, and Philip Steinke, AMD At DAC 2022, Google Cloud, AMD, and Calibre Design Solutions presented an EDA in the cloud solution that enables companies to access virtually unlimited compute resources when and as needed to optimize their design and verification flows. If your company is considering addin... » read more

AI-Driven Big Data Analytics Enables Actionable Intelligence, Improving SoC Design Productivity


As the latest systems on chip (SoCs) grow in size and complexity, a vast amount of design data is generated during verification and implementation. Design data is business critical and, with the proliferation of artificial intelligence (AI) use in chip design, provides designers an opportunity to carry forward learnings and insights with every new design. To achieve first-pass success deliverin... » read more

EDA, IP Revenue Way Up


EDA and semiconductor IP sales grew 17.5% to $3.75 billion in Q2, the highest growth in more than a decade, fueled by more complex designs and the need for advanced design and verification tools. Demand for nearly every segment tracked in SEMI's Electronic Design Market Data (EDMD) report was up, including services, which grew 23.2% in Q2 — the most recent statistics available in. That cou... » read more

EVs Raise Energy, Power, And Thermal IC Design Challenges


The transition to electric vehicles is putting pressure on power grids to produce more energy and on vehicles to use that energy much more efficiently, creating a gargantuan set of challenges that will affect every segment of the automotive world, the infrastructure that supports it, and the chips that are required to make all of this work. From a semiconductor standpoint, improvements in th... » read more

IC Architectures Shift As OEMs Narrow Their Focus


Diminishing returns from process scaling, coupled with pervasive connectedness and an exponential increase in data, are driving broad changes in how chips are designed, what they're expected to do, and how quickly they're supposed to do it. In the past, tradeoffs between performance, power, and cost were defined mostly by large OEMs within the confines of an industry-wide scaling roadmap. Ch... » read more

Week In Review: Design, Low Power


Tools and IP Renesas introduced a new microprocessor that enables artificial intelligence to process image data from multiple cameras. "One of the challenges for embedded systems developers who want to implement machine learning is to keep up with the latest AI models that are constantly evolving,” said Shigeki Kato, Vice President of Renesas' Enterprise Infrastructure Business Division. �... » read more

Toward Domain-Specific EDA


More companies appear to be creating custom EDA tools, but it is not clear if this trend is accelerating and what it means for the mainstream EDA industry. Whenever there is change, there is opportunity. Change can come from new abstractions, new options for optimization, or new limitations that are imposed on a tool or flow. For example, the slowing of Moore's Law means that sufficient prog... » read more

The End Of Closed EDA


In a previous life, I was a technologist for a large EDA company. One of my primary responsibilities in that position involved talking to a lot of customers to identify their pain points, and what new tools we could develop that would ease their problems. You would think that would be an easy task, but it certainly was not the case. For example, if you ask a developer what their biggest frus... » read more

Strengthening The Global Semi Supply Chain


Within the semiconductor ecosystem, there are a number of dynamics pointing to the need for new ways of partnering in more meaningful ways that bring resiliency to the global semiconductor supply chain. One of these is the move to bespoke silicon, stemming from a shift in the companies that create most SoCs today -- the hyperscalar cloud providers. These market leaders know their workloads so w... » read more

Can ML Help Verification? Maybe


Functional verification produces an enormous amount of data that could be used to train a machine learning system, but it's not always clear which data is useful or whether it can help. The challenge with ML is understanding when and where to use it, and how to integrate it with other tools and approaches. With a big enough hammer, it is tempting to call everything a nail, and just throwing ... » read more

← Older posts Newer posts →