Comprehensive software development kit and toolchain for NexusEdge AI accelerators with model optimization, deployment, and debugging capabilities.
Convert models from TensorFlow, PyTorch, ONNX to optimized NexusEdge format with automatic quantization and layer fusion.
Lightweight inference engine with C/C++ APIs for model loading, execution, and memory management on target hardware.
Performance analysis tools for measuring inference latency, throughput, power consumption, and memory usage.
Layer-by-layer output inspection, numerical accuracy verification, and visualization tools for model debugging.
TensorFlow, PyTorch, ONNX, TFLite, Keras
INT8, INT16, mixed precision, post-training quantization
Layer fusion, pruning, knowledge distillation
C, C++, Python APIs with examples
Linux, RTOS, bare-metal support
API reference, tutorials, example projects
Develop and train your model using standard ML frameworks
Quantize and optimize model for target NexusEdge hardware
Deploy optimized model to device using runtime library
Measure performance and iterate on optimization
LoRa/Industrial edge AI processor for IoT and smart agriculture applications.
View NexusEdge-L Specifications →Space-tolerant edge AI accelerator with radiation-hardened design.
View NexusEdge-S Specifications →Automotive ADAS edge AI processor with ASIL-D safety certification.
View NexusEdge-A Specifications →Evaluation boards with pre-installed SDK, reference designs, and sample applications for rapid prototyping.
Order Dev Kits →Complete API references, user guides, application notes, and compliance documentation.
View Documentation →Need custom SDK features or specialized tools? We offer tailored development solutions.
Learn About Custom Services →Download the SDK and access comprehensive documentation, tutorials, and example projects.