How to deal with integration challenges at the front end of the design process.
Neil Songcuan, senior product marketing manager at Synopsys, examines the hidden time savings from using an FPGA prototype platform for IP validation and software development, in addition to hardware design. While FPGA prototypes are a well known way of speeding up hardware design, their value in IP validation and software development for an integrated SoC is just beginning to surface.
Lines blur as processors are added into traditional FPGAs, and programmability is added into ASICs.
Costs of developing a complex chip could run as high as $1.5B, while power/performance benefits are likely to decrease.
Experts at the Table, part 1: Why machine learning works in some cases and not in others.
Continued integration is no longer the natural way forward for semiconductors. What needs to happen to make it easier?
What AI and deep learning are good for, what they’re not good for, and why accuracy sometimes works against these systems.
Lines blur as processors are added into traditional FPGAs, and programmability is added into ASICs.
Goal is to improve quality while reducing time to revenue, but it’s not always so clear-cut.
But working with this architecture has some not-so-obvious pitfalls, and new tools licensing options may be necessary.
What AI and deep learning are good for, what they’re not good for, and why accuracy sometimes works against these systems.
So far there are no tools and no clear methodology to eliminating bugs. That would require understanding what an AI bug actually is.
When does a large amount of data become Big Data, and could system-level verification benefit from it?
Acceleration of advanced processes, skyrocketing complexity and cost, and concerns about IP availability are raising some difficult questions.
Part 1: New opportunities and potential pitfalls in automotive, 5G, connected intelligence and infrastructure as a service in EDA.
 |
 |
 |
 |
 |
 |
 |
 |
 |
|
Leave a Reply