Quantized neural network deployment, TinyML edge inference, and hardware-accelerated computer vision models engineered for microcontrollers, FPGAs, and silicon NPUs. Bridging state-of-the-art machine learning with low-power silicon constraints.
Tailored machine learning services designed specifically for constrained embedded hardware, edge accelerators, and silicon prototyping.
Deploying lightweight neural nets onto resource-constrained MCUs with under 256KB RAM using TensorFlow Lite Micro, Edge Impulse, and CMSIS-NN kernels.
Converting heavy FP32 weights into post-training INT8 or Quantization-Aware Training (QAT) structures with weight pruning and structural sparsity.
Real-time visual detection pipelines tailored for edge cameras, industrial defect inspection, presence detection, and gesture interfaces on edge SoCs.
Synthesizable systolic array MAC engines and custom Verilog/SystemVerilog convolution units on AMD Xilinx and Intel FPGA platforms.
Always-on acoustic sensing, voice wake-word triggers, and vibration anomaly classification for predictive maintenance in industrial automation.
End-to-end continuous benchmarking, hardware-in-the-loop validation, automated regression testing, and over-the-air model updating infrastructure.
Have specific silicon targets, sensor requirements, or latency constraints? Our engineering team works directly with your hardware specs to deliver production-grade models.
How we transition machine learning models from floating-point training to low-power edge silicon execution.
Discuss your target silicon, latency requirements, or model quantization needs with our embedded AI specialists.