AI software encompasses an entire spectrum of types and applications, from speech processing and embedded applications such as in cameras and robots, through to data center systems requiring high power and performance qualities. IDTechEx's portfolio of Semiconductors, Data Centers, AI Research Reports and Subscriptions covers the latest developments, including AI hardware and computing trends.
AI learning subsets
Within the field of AI and the computer systems that can mimic human intelligence, there are multiple subsets. Machine learning allows for computer learning from data and over-time improvements that can happen without specific programming. Machine learning is largely enabled by neural network technology, which simulates networks within the human brain through which connections and associations are made. These artificial neural networks are responsible for computational natural language processing and decision-making capabilities.
Deep learning is a final subset of AI, described by IDTechEx as incorporating many layers of neural networks and is the stage of AI technology that has been responsible for many advancements within the sector over the past decade. However, deep learning is not the only means of enabling AI as it is currently known, as reinforcement learning, or learning through experience and interaction, is another route to achieving high-performing AI technologies. IDTechEx's report, "AI Chips for Edge Applications 2026-2036: Technologies, Markets, Forecasts", goes into more detail.
Cloud, edge, and AI chips
Cloud and edge AI characteristics differ in terms of power and complexity. Edge AI does not require devices to be connected to the cloud and is more concerned with running AI models as opposed to training them. Data can be processed locally without the need for consistent communication with the cloud meaning applications can be run independently and in real-time. Edge computing also uses simpler systems and less power consumption. Cloud AI, on the other hand, is where most AI training takes place, requiring higher power capabilities and incurring greater costs. IDTechEx's report, "AI Chips for Data Centers and Cloud 2025-2035: Technologies, Market, Forecasts", covers cloud computing in greater depth.
Edge AI is described as being an enabler of new systems with increased efficiency across many markets, including automotive, consumer electronics, and sensors. Fast decision making, reduced data transfer, and lower bandwidth requirements are all benefits of edge AI within these sectors, according to IDTechEx, as well as its ability to allow for advanced AI features such as real-time assistance in vehicles and smartphones.
The hardware behind AI is just as important in allowing the tech to perform. IDTechEx's AI Chips for Edge Applications report categorizes AI chips and provides benchmarking across CPUs, GPUs, and NPUs. NPUs are outlined by IDTechEx as having specifically been designed for AI applications, best-suited for edge applications, real-time inference, and mobile AI, whilst also being known for their high energy efficiency capabilities. While the overall power of CPUs and GPUs may be greater and suited to a wider range of applications, including PCs, gaming, and simulations, they are also associated with higher energy usage and power demand. IDTechEx describes the necessity of matching the correct processor to an application for the best performance.
IDTechEx's latest report, "AI-Driven Battery Technology 2027-2037: Technologies, Innovation, Applications and Opportunities", also covers the use of AI across batteries for lifecycle optimization, cell-testing, material selection, and more.
For more information, visit IDTechEx's portfolio of Semiconductors, Data Centers, AI Research Reports and Subscriptions for the latest developments across the entire computing sector.
