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…
Category: Artificial Intelligence
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,…
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…
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…
Spiking Neural Networks
Spiking Neural Networks, or SNNs, are a type of artificial neural network designed to work more like the human brain. They process information using spikes, which are brief electrical pulses, rather than continuous signals. This makes them more energy efficient and suitable for certain tasks. SNNs are particularly good at handling data that changes over…
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…