Information-Efficiency Architecture for AI Workloads
Public technology names: MSML-LLMC, MSML/IDDO AI applications
Challenge
AI workloads place pressure on model storage, working memory, context movement, retrieval, network traffic, inference and edge-device limits. The project examines whether structural information representation can reduce workload-specific information burden without moving task quality outside an agreed tolerance.
Why the problem is difficult
Each workload has different constraints, and any efficiency gain must be proven not to move task quality outside an explicit tolerance.
DBRF OEM contribution
Workload decomposition, AI-systems architecture, candidate insertion points, experimental design, quality-equivalence criteria, prototype paths and technical/economic measurement design.
Approach
- 01Decompose workloads and identify candidate insertion points
- 02Define quality-equivalence criteria and baselines
- 03Prototype structural representation paths
- 04Design telemetry, treatment and fallback
Work packages
- Model and checkpoint representation
- KV-cache and long-context traffic
- RAG corpus structure and context selection
- Agent memory and repeated tool/context records
- Multimodal preprocessing
- Model distribution to edge environments
- Baseline, treatment, fallback and telemetry design
Outputs
Skills assembled
Evidence and validation boundary
Performance is described only at the level supported by a named workload and an evidence record. No universal performance transfer is implied.
Claims discipline: Do not reuse public target figures as achieved DBRF results. Mark unverified performance numbers as targets or omit them.
IP / rights statement
Developed as an OEM scientific and engineering contribution. Technology and intellectual-property rights remain with the applicable contracting or rights-holding organisation. DBRF does not claim ownership unless expressly stated in a verified record.