PyTorch & JAX
Primary research frameworks — PyTorch for production cortical networks, JAX for custom gradient computation and neural ODE research.
Hugging Face
Transformer foundation models with our cortical fine-tuning pipeline — custom heads, tokenisers, and hierarchical attention modules.
TensorRT & ONNX
Production inference optimisation — hardware-specific kernel fusion, layer optimisation, and mixed-precision compilation for cortical architectures.
Weights & Biases
Experiment tracking, hyperparameter sweeps, model versioning — every cortical architecture experiment tracked and reproducible.
Triton & CUDA
Custom GPU kernels for cortical attention variants, sparse connectivity, and memory-bandwidth-optimised hierarchical inference.
Ray & DeepSpeed
Distributed training infrastructure — ZeRO optimisation, tensor/pipeline parallelism for large cortical foundation models.
Foolbox & ART
Adversarial robustness evaluation and certified defence implementation — systematic red-teaming of every deployed cortical model.
Cloud & Edge
Multi-cloud deployment (AWS, GCP, Azure) and edge compilation (TFLite, CoreML, ONNX) for cortical AI at any deployment target.