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5 results for “hardware”
Model Compression
Model compression is a set of techniques that reduce the size, memory footprint, and computational cost of machine learning models while preserving predictive accuracy, enabling deployment on resource-constrained hardware.
ONNX (Open Neural Network Exchange)
An open standard format for representing machine learning models that enables interoperability between deep learning frameworks, runtimes, and hardware platforms.
OpenVINO
OpenVINO is an open-source toolkit developed by Intel for optimising and deploying deep learning inference across Intel hardware, including CPUs, GPUs, Neural Processing Units, and FPGAs, with broad support for major AI frameworks and model formats.
Stable Diffusion
Stable Diffusion is an open-source latent diffusion model developed by Stability AI that generates high-quality images from text prompts, running efficiently on consumer-grade hardware.
Tensor Processing Unit
A tensor processing unit (TPU) is a custom application-specific integrated circuit developed by Google for accelerating machine learning workloads, particularly neural network training and inference.