Features
Reconfigurable dataflow architecture (XPU)
Supports FP8 to FP64 precision
Dense and irregular workload optimization
Minimizes data movement for energy efficiency
FPGA prototype (Grasshopper) for early testing
Cluster design (Monolith) for scaling as a single machine
Aimed at AI discovery loops (propose, simulate, test, learn)
Designed for training, simulation, and verification
Low-energy AI inference and training
Hardware for recursive self-improvement and superintelligence
Automated neural architecture search (NAS)
INT8 and FP16 quantization
Hardware-aware optimization for NVIDIA GPUs
Model compression and pruning
NVIDIA TensorRT integration
Benchmarking and performance profiling
Deployment to cloud, edge, and mobile
Custom training with NAS-driven architectures
Computer vision model optimization
Deci AI Studio for model development
Automatic compilation pipeline for target hardware
