Category: AI Infrastructure

TinyML Frameworks

TinyML frameworks are specialised software tools that help developers run machine learning models on very small and low-power devices, like sensors or microcontrollers. These frameworks are designed to use minimal memory and processing power, making them suitable for devices that cannot handle large or complex software. They enable features such as speech recognition, image detection,…

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…

Optical Neural Networks

Optical neural networks are artificial intelligence systems that use light instead of electricity to perform calculations and process information. They rely on optical components like lasers, lenses, and light modulators to mimic the way traditional neural networks operate, but at much faster speeds and with lower energy consumption. By processing data with photons rather than…

Neuromorphic Computing

Neuromorphic computing is a type of technology that tries to mimic the way the human brain works by designing computer hardware and software that operates more like networks of neurons. Instead of following traditional computer architecture, neuromorphic systems use structures that process information in parallel and can adapt based on experience. This approach aims to…

Input Validation Frameworks

Input validation frameworks are software tools or libraries that help developers check and control the data entered into a system. They ensure that input from users or other systems meets specific rules, such as correct format, length, or required fields. By filtering out invalid or harmful data, these frameworks protect applications from errors and security…

Shard Synchronisation

Shard synchronisation is the process of keeping data consistent and up to date across multiple database shards or partitions. When data is divided into shards, each shard holds a portion of the total data, and synchronisation ensures that any updates, deletions, or inserts are properly reflected across all relevant shards. This process is crucial for…

Differentiable Neural Computers

Differentiable Neural Computers (DNCs) are a type of artificial intelligence model that combines neural networks with an external memory system, allowing them to store and retrieve complex information more effectively. Unlike standard neural networks, which process information in a fixed way, DNCs can learn how to read from and write to memory, making them better…

Gas Optimization

Gas optimisation refers to the practice of reducing the amount of computational resources, known as gas, needed to execute transactions or smart contracts on blockchain platforms such as Ethereum. By optimising code and minimising unnecessary operations, developers can make transactions more efficient and less expensive. Gas optimisation is important because high gas usage can lead…

Layer 0 Protocols

Layer 0 protocols are foundational technologies that enable the creation and connection of multiple blockchain networks. They provide the basic infrastructure on which other blockchains, known as Layer 1s, can be built and interact. By handling communication and interoperability between different chains, Layer 0 protocols make it easier to transfer data and assets across separate…

Plasma Scaling

Plasma scaling refers to adjusting the size or output of a plasma system while maintaining its performance and characteristics. This process is important for designing devices that use plasma, such as reactors or industrial machines, at different sizes for various purposes. By understanding plasma scaling, engineers can predict how changes in size or power will…