ML Optimization
Refactor and optimize models to reduce latency, memory, compute, or footprint while keeping performance under control. Techniques include quantization, pruning, distillation, and architectural tuning.
We support teams in building efficient, hardware-ready ML pipelines. Our work centers on model optimization, FPGA acceleration, and reproducible engineering practices, helping teams replace ad-hoc steps with clear, validated workflows from prototype to deployment.
Refactor and optimize models to reduce latency, memory, compute, or footprint while keeping performance under control. Techniques include quantization, pruning, distillation, and architectural tuning.
Guide the pipeline that takes your models from research environments into FPGA or edge devices, using clean, reproducible workflows.
Translate algorithms into high-performance hardware using High-Level Synthesis. We help with pipeline design, memory architecture, parallelization strategies, and optimization for timing, resource usage, and throughput.
Structure your ML-to-hardware processes, tools, and responsibilities so teams can move with clarity instead of ad-hoc scripts.
Combine tailored training with project-focused consulting: learning while building something real.
If you’re exploring ML and hardware, working with FPGAs or edge devices, or translating algorithms into efficient HLS implementations, we can help you choose the right path. From model optimization to hardware-friendly redesign, pipeline architecture, and reproducible workflows, we work with you to shape a collaboration that fits your technical context, constraints, and goals.
Share a short description of your project, your team, and your timeframe, and we’ll schedule a call.
Contact for consulting