KaleidoForge bridges machine learning and hardware acceleration through education, consulting, and applied research, turning complex ideas into efficient, deployable solutions.
Three pillars connecting ML research with real hardware.
Engineer-focused programs blending ML concepts with real FPGA workflows. From foundational modules to intensive 2-week guided labs with hardware.
EducationSupport teams building efficient, hardware-ready ML pipelines. Model optimization, FPGA acceleration, and reproducible engineering workflows.
EngineeringResearch threads around efficient ML, FPGA acceleration, compact models, and ML-to-hardware deployment. Case studies and experiments.
Research