Category: Model Optimisation Techniques

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…

Knowledge Amalgamation Models

Knowledge amalgamation models are methods in artificial intelligence that combine knowledge from multiple sources into a single, unified model. These sources can be different machine learning models, datasets, or domains, each with their own strengths and weaknesses. The goal is to merge the useful information from each source, creating a more robust and versatile system…

Neural Network Sparsification

Neural network sparsification is the process of reducing the number of connections or weights in a neural network while maintaining its ability to make accurate predictions. This is done by removing unnecessary or less important elements within the model, making it smaller and faster to use. The main goal is to make the neural network…

Neural Network Compression

Neural network compression is the process of making artificial neural networks smaller and more efficient without losing much accuracy. This is done by reducing the number of parameters, simplifying the structure, or using smart techniques to store and run the model. Compression helps neural networks run faster and use less memory, making them easier to…

Knowledge Distillation Pipelines

Knowledge distillation pipelines are processes used to transfer knowledge from a large, complex machine learning model, known as the teacher, to a smaller, simpler model, called the student. This helps the student model learn to perform tasks almost as well as the teacher, but with less computational power and faster speeds. These pipelines involve training…

Neural Network Quantization

Neural network quantisation is a technique used to make machine learning models smaller and faster by converting their numbers from high precision (like 32-bit floating point) to lower precision (such as 8-bit integers). This process reduces the amount of memory and computing power needed to run the models, making them more efficient for use on…