OVERVIEW
We bring AI inference processes to edge devices via FPGA and SoC platforms. We offer solutions for situations where low latency, stable timing, and power efficiency are critical. Depending on your needs, we embed your AI model directly into custom FPGA logic or integrate and run it at the system level on an SoC. This ensures your AI systems operate precisely where the data is generated.
KEY SERVICES
Model-to-hardware Acceleration
We take your AI algorithm (for example, a Python, PyTorch, or TensorFlow model) and implement it directly as custom FPGA logic. This allows us to achieve maximum performance and low-latency inference on the hardware.
System-level AI Integration
We integrate your AI model into the SoC and run it at the system level (PS + PL), combining processor-based flexibility with FPGA acceleration where it counts.
Edge-optimized Inference
Quantization and fixed-point implementation to fit models within FPGA resource and power budgets while preserving accuracy.
Real-time Data Pipelines
Direct connection of AI inference to sensor and interface data paths (camera, RF, high-speed I/O), keeping the full pipeline on-chip.