SEMICONDUCTOR AND EDA DATA MANAGEMENT

Every design file, in full view.

EDA data management for semiconductor teams: Diskover indexes design, simulation, and verification data across every filer, object store, and archive, in place. Engineers find the run they need, CAD and IT see what is safe to move, and tapeout data is tagged for protection.

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103B+ files

in one searchable index at a semiconductor company

Diskover semiconductor customer

36 PB

of storage across 17 locations

Diskover semiconductor customer

MILLIONS

saved annually by controlling storage utilization across sites

Diskover semiconductor customer

THE PROBLEM

Engineers see the tip. Filers hold the rest.

Every tapeout leaves waveforms, logs, and old runs behind. They pile up on fast storage, tapeout after tapeout, while the next regression waits for room. Much of it can be regenerated, and nobody plans to open it again.

Diagram of EDA data on a filer: current RTL and active regressions above the surface, waveforms, logs, core dumps, and old runs below.

HOW IT WORKS

From scattered files to clear action.

01

CONNECT

Point Diskover at your filers, object stores, cloud buckets, and archive tiers, including symlinked and automounted paths. Indexing runs in place, so design data stays where the flow and the scheduler expect it.

Global visibility

02

CATALOG

Every file becomes a searchable record with rich metadata, such as name, path, owner, and dates, enriched with project and block names from the path, directory lineage, and classification tags.

Metadata catalog

03

ORGANIZE

Tag by project, block, tool, site, and run status. Build saved searches for what teams ask every week, such as scratch from canceled runs or waveforms past their review window.

Data intelligence

04

ACTIVATE

Run cleanup, tiering, and archive by policy with a full audit trail, and deliver curated design and regression metadata to analytics and AI pipelines through the API.

Orchestration

BUILT FOR EDA

Diskover speaks EDA.

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Knows your design files.

RTL, waveforms, logs, GDSII, OASIS, LEF/DEF, and libraries, grouped into the categories your teams use.

Traces failed jobs.

Follow a failed run across symlinked filers in seconds, from the job to every file it left behind.

Adds business context.

Plugins link files to Jira projects, owners, and cost centers, so storage shows up by project.

Self-service archive.

Engineers archive their own data, within the rules IT sets, without opening a ticket.

WHO FEELS IT

Three teams. Three different pains.

DESIGN • VERIFICATION

“Which run broke the build?”

  • Failed jobs are hard to trace across filers.
  • Every regression leaves FSDB and log files behind.
  • Symlinks hide where data really lives.

With Diskover: trace any run to every file it left behind, across every filer.

CAD • IT

“Why is the filer full again?”

  • Flash filers hold old regressions nobody opens.
  • Quotas fill up right before tapeout.
  • Old project areas have no owner to approve cleanup.

With Diskover: see which old regressions can move, and move them by policy before the next tapeout.

IP SECURITY • COMPLIANCE

“Where does our restricted IP live?”

With Diskover: see where restricted IP sits, who owns it, and which site holds it, with an audit trail.

WHY IT MATTERS

Spend on tapeouts, not on old runs.

Gartner expects NAND flash prices to rise 202% in 2026. When old regressions fill flash filers, every new project pays for them. With context on every file, teams put a cost on storage, keep flash for active work, and know where restricted IP lives.

Explore data intelligence

Free up flash.

Old regressions and taped-out projects move to lower tiers by policy, so flash holds active work.
Data tiering

Cost every project.

See storage cost by project, team, or site, so each design group knows what it spends.
Chargeback

Tape out on schedule.

Quotas stay clear before tapeout, so regressions run instead of waiting for room.
Saved searches

Protect your IP.

Know where restricted IP lives, who owns it, and which site holds it, for every audit.
Data governance

AI READINESS

Prepare design data for AI before it reaches a model.

Explore AI-ready data

01

TAG

AutoTag rules label files as they are indexed, such as passing runs, golden datasets, and restricted IP. Path tokens turn folder names into search fields.

02

CURATE

Select regressions, simulation runs, and logs by any tag or metadata, such as project, tool, or result. Save the selection as a search to run again for the next model.

03

GOVERN

Rules leave restricted IP and customer designs out of the training set. Every dataset keeps an audit trail.

04

DELIVER

Send curated file lists to your AI and analytics platforms as Parquet, or copy the files to where the model runs, by policy.

REAL-WORLD USE CASE

103 billion files. One searchable index.

A $314B semiconductor company, across 4 global locations and 600 projects a day.

103B+

files indexed

100+ PB

of design data

~8,000

storage volumes

~4,000

engineers and data users

THE CHALLENGE

  • Design data spread across 4 locations and thousands of volumes.
  • Cleanup depended on manual, project-by-project work.
  • Storage use was hard to control across sites.

HOW THEY USE DISKOVER

  • One index across every site and volume.
  • Diskover opened to engineers, not only IT, so data owners clean up their own data.
  • Automated cleanup policies, run on a schedule.

$250K a year

in opex saved through automated cleanup policies

Millions

saved each year by controlling storage use across sites

600

projects processed per day, from one index

See how teams like yours did it

FAQ

Common questions.

Scattered data points flowing into a single stream that becomes organized data paths across a world map.

See it on your own design data.

We will index a sample of your filers and show you what is recoverable against your current storage spend.

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