Case Studies & Testimonials

Google
Nvidia
Samsung

Static Sign-Off Best Practices DAC Panel Proceedings. Panelists from Google, Nvidia and Samsung discussed their static sign off goals, their selected best practices and technologies used to support those goals, and their results in accelerating early functional verification & sign-off of digital designs.

See presentation videos & transcripts >>

Hailo successful static sign-off for edge AI processor using Real Intent Meridian CDC & Ascent Lint. Includes: Ascent Lint shortened their verification time an estimated five weeks by reducing simulation debug. Additionally, Hailo was able to get good quality results from Meridian CDC within the first two weeks. Also, the ease of integration of IP vendor constraints avoided noise in CDC runs.

Read article >>

Meridian RDC‘s engine is specifically customized for RDC.  This makes Meridian RDCs reports much simpler to go through.  9x fewer false violations [vs. a competitor’s RDC tool] make for a more efficient debug process for us.

“The Meridian RDC runtime was 3x to 4x faster than the [competitor’s RDC tool] runtime for the same design.”

Read Article >>

Western Digital developed an enhanced clock & reset verification methodology to make the Meridian CDC static sign-off tool aware of Western Digital’s clock and reset architectures. The results were: 1) They exponentially reduced their CDC sign-off noise and debug effort, and 2) They caught corner-case reset synchronization issues because only real violations were flagged with the enhanced methodology.

Read article >>

Nvidia clock domain crossing CDC handshake advanced sign-off methodology minimizes the additional engineering effort required to complete full CDC sign-off, by ensuring unsafe scenarios are consistently identified within Real Intent Meridian CDC’s full violation report. This article summarizes the methodology and results shown in the DAC presentation.

Read article >>

Ascent AutoFormal enabled a 30 percent reduction in logic simulation time. This simulation reduction was based on AutoFormal’s ability to identify the root cause error as a primary failure, with other violations detected as warnings.”  – Atsunori Machida, Fujitsu Kyushu Network Technologies

Read Article >>