From smartphones to media and business applications, AI is everywhere, and its influence is growing rapidly. However, one aspect that often gets overlooked is the significant energy consumption associated with AI computing. While AI is advancing, it's important to acknowledge its environmental impact. A server farm running AI processors can consume up to 160% more energy than a traditional server farm.

This spike in power usage is largely due to the complexity of AI processors, which contain multiple cores designed to handle intensive computations. These processors are crucial for managing deep learning algorithms, including Large Language Models (LLMs) like those powering popular AI systems such as ChatGPT. As AI applications grow in scale and sophistication, addressing the energy consumption and carbon footprint of AI infrastructure becomes a key challenge. Finding ways to optimise energy efficiency while maintaining performance is critical for the sustainable development of AI technology. The tech industry must balance innovation with sustainability to continue to harness the power of AI without compromising environmental responsibility.

The good news is the technology sector is witnessing significant innovation in AI, particularly in reducing power consumption and improving efficiency. This effort involves not only making AI processors more energy-efficient but also reconsidering how and where AI processing occurs. Traditionally, most AI processing has been handled in large data centres, commonly referred to as the Cloud. However, it’s increasingly clear that not all AI tasks need to be processed at these centralised server farms. Many AI functions can now be performed locally using the processing power available on the user’s device or nearby gateways that connect devices to the Internet.

This approach, known as AI Edge computing, brings notable advantages. By processing data closer to where it is generated, AI Edge computing reduces latency, leading to faster responses and improved real-time performance. It also decreases the reliance on Cloud resources, resulting in lower energy consumption and reduced operational costs.

Additionally, this method enhances privacy and security, as sensitive data doesn’t always need to be transmitted to the Cloud for processing. Overall, AI Edge computing is becoming a crucial innovation, offering a more sustainable, efficient, and responsive solution for handling AI tasks in various applications.

Advantages of AI Edge computing

Reduced latency: AI Edge computing processes data locally, near the source of data generation (like IoT devices), which significantly reduces latency or delay compared to Cloud computing. This is crucial for real-time applications, such as material handling automated guided vehicles (AGV) used in smart factories and warehouses or healthcare monitoring systems.

Enhanced reliability: AI systems at the Edge can continue to function even when there is limited or no cloud connectivity. This makes Edge computing a more reliable option in remote areas or in scenarios where constant internet access isn't guaranteed.

Bandwidth Efficiency: In AI Cloud computing, substantial amounts of data need to be transmitted to centralised data centres, which consumes substantial bandwidth. Edge computing reduces the amount of data that must be sent to the Cloud, conserving network resources and enabling faster decision-making.

Cost efficiency: by reducing the need for constant data transfer and Cloud-based computation, Edge computing can lower operational costs associated with bandwidth and Cloud storage fees.

Improved data privacy and security: since data is processed locally at the Edge, less sensitive information is transmitted to centralised servers, lowering the risk of data breaches or unauthorised access. This is particularly beneficial in industries like healthcare and finance.

Scalability and flexibility: Edge computing allows AI to be deployed in a wide range of environments, from urban areas to remote or industrial locations. This makes it easier to scale AI powered devices and services without overwhelming Cloud infrastructure.

Sustainability benefits of AI Edge computing

Energy efficiency: Edge computing reduces the reliance on large-scale, energy-intensive data centres by distributing computing tasks across Edge devices. Processing data locally requires less energy than transmitting and processing it in centralised Cloud servers, thus reducing overall energy consumption.

Lower carbon emissions: by minimising data transmission and the need for constant Cloud-based processing, AI Edge computing can help lower the carbon footprint associated with large data centres, which consume significant amounts of electricity and contribute to greenhouse gas emissions.

Localised resource use: Edge devices typically require less power than large Cloud infrastructures. Moreover, they often operate on renewable energy sources or energy-efficient hardware, further reducing environmental impacts.

Implementing AI at the Edge

If you're planning to integrate AI into your next product, Anglia Unicorn is here to help. Our engineering teams have vast experience in applying AI across various sectors, allowing us to seamlessly integrate AI features into a wide range of applications. We collaborate with some of the world’s top semiconductor companies that are leading the AI revolution. These partnerships enable us to bring cutting-edge products to the market, such as vision, motion, and bio-sensors equipped with machine learning (ML) cores, cellular communication modules with Edge Intelligence, and Microcontroller (MCU) and Microprocessor (MPU) units featuring integrated AI capabilities. Additionally, we provide the complete toolchains and development ecosystems necessary to accelerate your product’s time-to-market. By offering comprehensive support throughout the development process, we ensure that your product launch is successful. Whether it's enhancing functionality with AI or streamlining communication through intelligent modules, Anglia Unicorn has the expertise to help you stay ahead in the rapidly evolving AI landscape.

A greener future

Cloud computing remains vital for large-scale data processing and storage, while AI Edge computing offers a greener solution for real-time, localised tasks. By combining the two, organisations can reduce energy consumption, cut carbon emissions, and decrease dependence on large, centralised data centres, thus supporting eco-friendly computing. This integration allows Edge computing to handle immediate tasks more efficiently, with Cloud resources augmenting as needed, creating a more sustainable and scalable system for modern applications.

This article originally appeared in the November/December 2024 issue of Startups Magazine. Click here to subscribe