Projects & Research
This page will gradually showcase a curated selection of research threads, tools, hardware workflows, and ongoing explorations around efficient ML, FPGA acceleration, compact models, and ML-to-hardware deployment.
Instead of listing scattered projects or technical notes, the goal is to present focused areas of work: from model compression to hardware dataflow, from embedded AI to reproducible pipelines.
Coming During 2026
Throughout 2026, this page will expand to include:
- ML-to-hardware workflow summaries
- FPGA deployment case studies
- Compression and optimization experiments
- Split learning and edge intelligence projects
- Hardware-aware benchmarks and profiling results
- Educational artifacts and reproducible examples
These entries will be published as structured, human-readable narratives.
Project Archive (Coming Later)
A small archive of past work (research papers, tools, prototypes, and experiments) will be added later on. Only a curated selection will be included, aligned with KaleidoForge’s mission and ML-to-hardware focus.