Resistive memory devices are a type of non-volatile memory that store data by changing the resistance of a material within the device. These devices use an electrical current to switch between different resistance states, which represent binary data such as 0s and 1s. Unlike traditional memory like RAM or hard drives, resistive memory retains information…
Tiny Machine Learning, often called TinyML, is the practice of running machine learning models on very small, low-power devices such as sensors or microcontrollers. These devices typically have limited memory and processing power, so the machine learning models must be small and efficient. TinyML enables smart features like voice recognition, gesture detection, or anomaly detection…
Edge analytics is the process of analysing data directly on devices or near where the data is created, instead of sending it to a central server or cloud. This allows for faster decision-making because the data does not have to travel far. It also reduces the amount of information that needs to be sent over…
AI for microgrids refers to the use of artificial intelligence to manage, optimise, and control small-scale local energy systems. Microgrids often combine renewable energy sources, batteries, and traditional power sources to supply electricity to a limited area such as a neighbourhood, campus, or industrial site. AI helps microgrids balance supply and demand, predict energy usage,…
AI for wearables refers to the use of artificial intelligence in devices that can be worn on the body, like smartwatches or fitness trackers. These devices use AI to process data from sensors, helping to monitor health, track activity, or provide personalised recommendations. The technology enables wearables to learn from user behaviour and adapt over…
AI for smart devices refers to the integration of artificial intelligence technologies into everyday electronic gadgets such as phones, speakers, TVs, and home appliances. This allows these devices to perform tasks that usually require human intelligence, like recognising voices, understanding commands, or learning user preferences. As a result, smart devices become more responsive, helpful, and…
Edge security hardening refers to strengthening the security of devices, systems, or applications that operate at the edge of a network, such as routers, gateways, or IoT devices. This process involves adding security measures like firewalls, secure authentication, regular software updates, and limiting network access to reduce vulnerabilities. The main goal is to protect edge…
Edge analytics pipelines are systems that process and analyse data directly on devices or local servers near where the data is generated, rather than sending all data to a central cloud or data centre. These pipelines often include steps like collecting, filtering, processing, and possibly sending only the most important data to the cloud for…
Edge AI model deployment is the process of installing and running artificial intelligence models directly on local devices, such as smartphones, cameras or sensors, rather than relying solely on cloud servers. This allows devices to process data and make decisions quickly, without needing to send information over the internet. It is especially useful when low…
Edge data caching strategies refer to methods used to store frequently accessed data closer to users, typically on servers or devices located near the edge of a network. This approach reduces the distance data needs to travel, resulting in faster access times and less strain on central servers. These strategies are important for applications that…