Category: Model Optimisation Techniques

Knowledge Sparsification

Knowledge sparsification is the process of reducing the amount of information or connections in a knowledge system while keeping its most important parts. This helps make large and complex knowledge bases easier to manage and use. By removing redundant or less useful data, knowledge sparsification improves efficiency and can make machine learning models faster and…

Graph Pooling Techniques

Graph pooling techniques are methods used to reduce the size of graphs by grouping nodes or summarising information, making it easier for computers to analyse large and complex networks. These techniques help simplify the structure of a graph while keeping its essential features, which can improve the efficiency and performance of machine learning models. Pooling…

Value Function Approximation

Value function approximation is a technique in machine learning and reinforcement learning where a mathematical function is used to estimate the value of being in a particular situation or state. Instead of storing a value for every possible situation, which can be impractical in large or complex environments, an approximation uses a formula or model…

Policy Iteration Techniques

Policy iteration techniques are methods used in reinforcement learning to find the best way for an agent to make decisions in a given environment. The process involves two main steps: evaluating how good a current plan or policy is, and then improving it based on what has been learned. By repeating these steps, the technique…

Resistive RAM (ReRAM) for AI

Resistive RAM (ReRAM) is a type of non-volatile memory that stores data by changing the resistance of a special material within the memory cell. Unlike traditional memory types, ReRAM can retain information even when the power is switched off. For artificial intelligence (AI) applications, ReRAM is valued for its speed, energy efficiency, and ability to…

AI Hardware Acceleration

AI hardware acceleration refers to the use of specialised computer chips and devices that are designed to make artificial intelligence tasks run much faster and more efficiently than with regular computer processors. These chips, such as graphics processing units (GPUs), tensor processing units (TPUs), or custom AI accelerators, handle the heavy mathematical calculations required by…

Edge AI Optimization

Edge AI optimisation refers to improving artificial intelligence models so they can run efficiently on devices like smartphones, cameras, or sensors, which are located close to where data is collected. This process involves making AI models smaller, faster, and less demanding on battery or hardware, without sacrificing too much accuracy. The goal is to allow…

Analog AI Accelerators

Analog AI accelerators are specialised hardware devices that use analogue circuits to perform artificial intelligence computations. Unlike traditional digital processors that rely on binary logic, these accelerators process information using continuous electrical signals, which can be more efficient for certain tasks. By leveraging properties of analogue electronics, they aim to deliver faster processing and lower…

Rollup Compression

Rollup compression is a technique used in blockchain systems to reduce the size of transaction data before it is sent to the main blockchain. By compressing the information, rollups can fit more transactions into a single batch, lowering costs and improving efficiency. This method helps blockchains handle more users and transactions without slowing down or…