Profile
Observe the memory state and identify the structures responsible for the active footprint.
DigiEmu Technology
Foam
Memory state optimisation.
Experimental memory-state optimisation for efficient, reconstructable and verification-aware AI infrastructure.
01
Overview
What it is
Foam explores how memory state can be represented more efficiently while preserving the information needed by higher-level reconstruction and verification processes.
02
Architecture
Memory is infrastructure, not an unlimited resource.
Foam investigates memory optimisation as a structured state problem. Instead of treating memory reduction as an opaque runtime trick, relevant state transitions and recovery requirements remain explicit.
Separate
Distinguish active, reconstructable and redundant memory representation.
Optimise
Reduce or reorganise the memory representation within the defined boundary.
Recover
Restore required state when downstream computation or verification needs it.
Measure
Compare footprint, recoverability and verification-relevant behavior against the baseline.
03
Workflow
Use less memory without making state invisible.
Foam is intended for controlled environments where memory pressure matters but state must remain understandable enough to support reconstruction and higher-level verification.
A
Profile
Capture a reproducible baseline of the workload and its memory behavior.
B
Map
Identify which memory structures are essential, reconstructable or redundant.
C
Optimise
Apply the selected memory-state optimisation strategy.
D
Restore
Recover state when the workload requires information that is no longer fully resident.
E
Evaluate
Measure memory reduction together with recovery and verification behavior.
04
Evidence
Memory reduction should remain measurable.
Foam experiments should preserve enough evidence to show what memory was reduced, what was recoverable and what trade-offs were introduced.
Baseline footprint
Memory use measured before optimisation.
State map
Classification of relevant memory structures inside the experiment boundary.
Optimised footprint
Memory use measured after the optimisation step.
Recovery behavior
Evidence describing whether required state can be reconstructed when needed.
Resource delta
Measured reduction or redistribution of the active memory footprint.
Verification basis
The baseline and retained evidence needed to compare experiment outcomes.
05
Boundaries
Memory efficiency is not proof of behavioral equivalence.
Foam is experimental memory infrastructure. Any claim about preservation or equivalence must remain limited to the state and measurements actually evaluated.
- Foam does not prove that every internal runtime state remains identical.
- Reduced memory use does not automatically imply equivalent model behavior.
- Recoverability applies only to state represented inside the defined optimisation boundary.
- Performance results depend on workload, runtime, model and system configuration.
- Experimental results must not be generalized beyond the measured evidence.
06
Pilot
Start with one measurable memory problem.
A Foam experiment should begin with a reproducible workload whose memory footprint can be measured before and after optimisation without changing the verification question midway through the experiment.
Measure baseline
Record the workload, memory footprint and state requirements before optimisation.
Define recovery boundary
Identify which state must remain resident, reconstructable or independently verifiable.
Optimise and compare
Apply the experiment, measure resource change and evaluate recovery against the baseline.
Baumgartner Systems
Start with one workflow.
Give us one AI-assisted process worth verifying.
Discuss a Pilot