As AJ intimated, when considering the power and constraints of the cloud relative to EDA, it's really in the "eye of the beholder".
For back-end tools we are challenged on two fronts - the amount of data to be transferred (bandwidth) and potentially the responsiveness of manipulations of graphical models (latency).
The closer we move up towards the front-end of the design flow, the less relevant these concerns become.
Indeed, the power of spinning up 1,000 servers to perform verification regressions in a day rather than a month is powerful, with no latency constraints of significance wrt the UI. Also, at higher levels of design abstraction, how much data really needs to be transferred?
The business model is likely to be the only real hurdle to rolling out a commercial SaaS solution. But in reality, that could likely be boiled down to mathematics and product packaging.
Xuropa.com is an EDA cloud computing service that is being used by its customers (including Synopsys and Cadence) for marketing and limited evaluations (in an enclosed, online environment).
It's hard to see EDA tools operating in an open, public environment like GoogleDocs but internal clouds for large enterprises should provide enough of a secure environment, I would imagine.
What are the engineering and design challenges in creating successful IoT devices? These devices are usually small, resource-constrained electronics designed to sense, collect, send, and/or interpret data. Some of the devices need to be smart enough to act upon data in real time, 24/7. Are the design challenges the same as with embedded systems, but with a little developer- and IT-skills added in? What do engineers need to know? Rick Merritt talks with two experts about the tools and best options for designing IoT devices in 2016. Specifically the guests will discuss sensors, security, and lessons from IoT deployments.