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5 results for “edge AI”
Edge AI
Edge AI is the deployment of artificial intelligence algorithms and inference workloads directly on local devices or edge computing nodes rather than in centralised cloud data centres, enabling low-latency, privacy-preserving, and bandwidth-efficient AI applications.
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.
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.
Quantisation
Quantisation is a model compression technique that reduces the numerical precision of a neural network's weights and activations from high-bit floating-point formats to lower-bit representations, decreasing memory usage and accelerating inference with minimal accuracy loss.
TinyML
TinyML is a field of machine learning focused on running machine learning models on microcontrollers and other resource-constrained edge devices that typically operate with milliwatts of power and kilobytes of memory.