Dynamic inference scheduling is a technique used in artificial intelligence and machine learning systems to decide when and how to run model predictions, based on changing conditions or resource availability. Instead of running all predictions at fixed times or in a set order, the system adapts its schedule to optimise performance, reduce delays, or save…
Category: Model Optimisation Techniques
Neural Activation Sparsity
Neural activation sparsity refers to the idea that, within a neural network, only a small number of neurons are active or produce significant outputs for a given input. This means that most neurons remain inactive or have very low activity at any one time. Sparsity can help make neural networks more efficient and can improve…
Adaptive Layer Scaling
Adaptive Layer Scaling is a technique used in machine learning models, especially deep neural networks, to automatically adjust the influence or scale of each layer during training. This helps the model allocate more attention to layers that are most helpful for the task and reduce the impact of less useful layers. By dynamically scaling layers,…
Feature Space Regularization
Feature space regularisation is a method used in machine learning to prevent models from overfitting by adding constraints to how features are represented within the model. It aims to control the complexity of the learnt feature representations, ensuring that the model does not rely too heavily on specific patterns in the training data. By doing…
Neural Gradient Harmonization
Neural Gradient Harmonisation is a technique used in training neural networks to balance how the model learns from different types of data. It adjusts the way the network updates its internal parameters, especially when some data points are much easier or harder for the model to learn from. By harmonising the gradients, it helps prevent…
AI Hardware Acceleration
AI hardware acceleration refers to the use of specialised computer chips or devices designed to make artificial intelligence tasks faster and more efficient. Instead of relying only on general-purpose processors, such as CPUs, hardware accelerators like GPUs, TPUs, or FPGAs handle complex calculations required for AI models. These accelerators can process large amounts of data…
TinyML Optimization
TinyML optimisation is the process of making machine learning models smaller, faster, and more efficient so they can run on tiny, low-power devices like sensors or microcontrollers. It involves techniques to reduce memory use, improve speed, and lower energy consumption without losing too much accuracy. This lets smart features work on devices that do not…
Data Pipeline Optimization
Data pipeline optimisation is the process of improving the way data moves from its source to its destination, making sure it happens as quickly and efficiently as possible. This involves checking each step in the pipeline to remove bottlenecks, reduce errors, and use resources wisely. The goal is to ensure data is delivered accurately and…
Blockchain Consensus Optimization
Blockchain consensus optimisation refers to improving the methods used by blockchain networks to agree on the state of the ledger. This process aims to make consensus algorithms faster, more secure, and less resource-intensive. By optimising consensus, blockchain networks can handle more transactions, reduce costs, and become more environmentally friendly.
Neural Network Efficiency
Neural network efficiency refers to how effectively a neural network uses resources such as time, memory, and energy to perform its tasks. Efficient neural networks are designed or optimised to provide accurate results while using as little computation and storage as possible. This is important for running models on devices with limited resources, such as…