AI Trustworthiness | According to the EU Ethics Guidelines (link), Trustworthy AI systems should be lawful, ethical, and robust throughout their entire life cycle. The foundations of Trustworthy AI lay upon 4 ethical principles, to ensure ethical and robust AI: (1) Respect for human autonomy, (2) Prevention of harm, (3) Fairness, and (4) Explicability. |
Cloud-Edge Continuum | The Cloud Edge Continuum is “an integrated environment that incorporates and blends together sensors, automated devices, edge computing, and centralised cloud computing in a way that is tailored to the specific needs of a use case and organisation. |
Pruning | Pruning AI models is the process of removing unnecessary or redundant parts of a neural network to reduce its size and complexity, and improve its efficiency and performance. |
Quantisation | Quantization is a model size reduction technique that converts model weights from high-precision floating-point representation to low-precision floating-point or integer representations, such as 16-bit or 8-bit. As a result the model size and inference speed can improve by a significant factor without sacrificing too much accuracy. Additionally, quantization will improve the performance of a model by reducing memory bandwidth requirements and increasing cache utilization. |