Category: AI Infrastructure

Serverless Computing Models

Serverless computing models allow developers to run code without managing servers or infrastructure. Instead, a cloud provider automatically handles server setup, scaling, and maintenance. You only pay for the computing resources you actually use when your code runs, rather than for pre-allocated server time. This approach makes it easier to focus on building applications rather…

Hybrid Cloud Architecture

Hybrid cloud architecture is a computing approach that combines private cloud or on-premises infrastructure with public cloud services. This setup enables organisations to move data and applications between environments as needed, offering flexibility and scalability. It helps businesses optimise costs, maintain control over sensitive data, and adapt quickly to changing needs.

Cloud and Infrastructure Transformation

Cloud and Infrastructure Transformation refers to the process organisations use to move their technology systems and data from traditional, on-site servers to cloud-based platforms. This shift often includes updating hardware, software, and processes to take advantage of cloud computing’s flexibility and scalability. The goal is to improve efficiency, reduce costs, and support new ways of…

AI-Driven Network Optimization

AI-driven network optimisation is the use of artificial intelligence to monitor, manage, and improve computer networks automatically. AI analyses large amounts of network data in real time, identifying patterns and predicting issues before they cause problems. This approach allows networks to adapt quickly to changing demands, reduce downtime, and improve efficiency without constant manual intervention.

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…

Field-Programmable Gate Arrays (FPGAs) in AI

Field-Programmable Gate Arrays, or FPGAs, are special types of computer chips that can be reprogrammed to carry out different tasks even after they have been manufactured. In artificial intelligence, FPGAs are used to speed up tasks such as processing data or running AI models, often more efficiently than traditional processors. Their flexibility allows engineers to…

Tensor Processing Units (TPUs)

Tensor Processing Units (TPUs) are specialised computer chips designed by Google to accelerate machine learning tasks. They are optimised for handling large-scale mathematical operations, especially those involved in training and running deep learning models. TPUs are used in data centres and cloud environments to speed up artificial intelligence computations, making them much faster than traditional…

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…

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,…