Cadence Plans to Apply Big Data to Optimize Workloads

Cadence, a leading intelligent system design provider, announced that it plans to apply big data-based applications and artificial intelligence to optimize workloads. The suite of these applications, called Verisium, is designed on JedAI (joint enterprise data and AI). 

Verisium marks a generational transition in EDA (electronic design automation) from single-run, single-engine algorithms to algorithms that use big data and AI to optimize numerous runs of multiple engines during a complete SoC design and verification campaign.

All verification data, including waveforms, coverage, reports, and log files, are transferred to the Cadence JedAI Platform after deploying Verisium. As a result of the ML models and the mining of additional proprietary metrics from this data, a new class of tools that significantly increase verification productivity is now possible.

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Cadence describes the first few Verisium apps as follows:

1. AutoTriage: Creates ML models to predict and categorize test failures with similar causes and aids in automating the triage of regression failures.

2. SemanticDiff: An algorithmic approach of comparing different source code revisions of an IP or SoC, classifying those revisions, and ranking which alterations affect the system’s behavior to find probable bug hotspots.

3. Debug: A system that offers interactive and post-process debug flows using waveform, schematic, driver tracing, and SmartLog technologies. It delivers a debug solution from IP to SoC and from single-run to multi-run.

4. WaveMiner: Artificial intelligence (AI) analyses waveforms from several runs to identify which signals, when they occur, are most likely to be the cause of a test failure.