Reducing Energy Demand of AI with Edge Computing
Traditional AI systems rely on large, centralized data centers, which consume vast amounts of electricity. These facilities house thousands of servers that run continuously, performing complex computations and requiring extensive cooling systems to prevent overheating. However, as AI workloads continue to expand, their energy consumption is becoming unsustainable.
In fact, data centers are already responsible for about 2% of global electricity consumption, and this number is expected to rise as AI models grow in size and complexity. Training a single large AI model, such as GPT-3, can use as much energy as hundreds of homes consume in a year.
How can we continue to benefit from AI while addressing the growing environmental cost of its infrastructure? One of the best strategies is to deploy edge computing, which shifts computing tasks away from centralized data centers to the edge of the network or onto local devices themselves. Edge computing brings computational power closer to the data source, and dramatically changes the economics of deploying AI at scale on small devices.
By processing AI workloads on devices such as smartphones, IoT sensors, and embedded systems, edge computing reduces the need to send large volumes of data to remote servers. This reduces the energy spent on data transmission and decreases reliance on power-hungry cloud servers.
In this article, we’ll explore how edge AI is reducing energy consumption and how innovations in chipsets and AI models are enabling more sustainable AI applications at the edge.
The Growing Energy Footprint of Centralized Data Centers
The rising energy demands of data centers are putting immense pressure on global power grids. These facilities require constant electricity to maintain operations. In regions where power supply is limited, this growing energy demand can lead to grid instability, making energy less accessible for homes and businesses.
Beyond resource consumption, the environmental consequences are significant. Some data centers rely on fossil fuels for electricity, contributing to carbon emissions and accelerating climate change. Even as companies invest in renewable energy, the rapid expansion of AI-driven workloads may outpace these efforts. If left unchecked, this trend will further strain natural resources and increase the carbon footprint of digital infrastructure.
How Edge Computing Is Reducing Energy Demand
One of the energy-saving benefits of edge computing is the reduction in reliance on cloud-based data centers. Traditionally, data centers handle the bulk of computational tasks, but with edge computing, workloads are distributed to gateways at the network edge, or increasingly directly to the nodes themselves – local devices like smartphones, IoT sensors, and embedded systems.
This alleviates the strain on data centers, which are major contributors to AI’s energy consumption. As more tasks are handled at the edge, the need for constant data transmission to and from the cloud diminishes, lowering energy usage and reducing the resources required to support the cloud infrastructure.
Additionally, edge computing reduces energy consumption by enabling more efficient use of resources. When data is processed locally, devices can avoid idle times that typically occur when waiting for cloud processing. This leads to less energy spent on maintaining connections, as there’s no need for continuous data transmission between the device and remote servers.
For example, in real-time applications like video surveillance, an edge device can analyze video feeds and only send relevant information to the cloud, such as alerts or specific events, rather than streaming the entire video continuously. This selective data transmission reduces the energy required for communication and storage, as only essential data is transmitted against vast amounts of raw data.
Power-Efficient Chipsets for the Edge
Several chipset designs have been developed to run AI applications on the edge. Here’s a quick look at some leading chipsets and their applications:
Arm Neoverse and Cortex-X Series
The Arm Neoverse and Cortex-X series are designed for high-efficiency AI processing, targeting a wide range of edge devices, including wearables, autonomous robots, and smart home systems.
These chipsets prioritize adaptive power scaling and low-power processing to efficiently handle edge AI workloads. By dynamically adjusting power consumption in real-time, these chips ensure that devices maintain optimal performance while minimizing energy usage.
Infineon PSOC Edge E8x Series
Infineon’s PSOC Edge E81, E83, and E84 microcontrollers are optimized for machine learning (ML) applications in IoT, consumer, and industrial devices.
Built on Arm Cortex-M55 and Cortex-M33 cores, they integrate Infineon’s NNLite neural network accelerator and, in the case of E83 and E84, an Arm Ethos-U55 micro-NPU for a significant boost in ML performance. The series supports voice and audio sensing, vision-based recognition, and human-machine interface (HMI) applications while maintaining ultra-low power consumption.
NXP i.MX 93
The NXP i.MX 93 chipset is optimized for real-time surveillance, smart home devices, and automotive applications. It supports low-precision AI processing (such as INT8 and INT4), which reduces memory usage and power consumption without sacrificing the accuracy required for real-time decision-making. The chipset is ideal for edge applications where fast processing is critical, but energy efficiency must also be maintained.
Renesas RZ/V2H
The Renesas RZ/V2H is tailored for industrial automation, wearables, and smart home systems. It features advanced AI-specific instruction sets and can accelerate tasks like matrix multiplications directly within the processor core, improving performance and efficiency.
The chipset’s adaptive power scaling (DVFS) adjusts power consumption based on workload demands, ensuring energy savings while maintaining the necessary performance for power-sensitive applications.
Silicon Labs xG26
The Silicon Labs xG26 family includes the MG26 multiprotocol SoC, BG26 Bluetooth LE SoC, and PG26 MCU, designed for smart home, industrial IoT, and battery-powered edge applications. Built on an ARM Cortex-M33 processor, the xG26 family features AI/ML acceleration for efficient low-power processing.
It supports multiple wireless protocols, including Matter over Thread and Bluetooth LE, and offers expanded Flash, RAM, and GPIO capacity. Security features such as Secure Vault and ARM TrustZone help protect connected devices, while pin compatibility with previous Silicon Labs chipsets simplifies development and migration.
STMicroelectronics STM32 Family
The STM32 series offers AI-capable microcontrollers optimized for low-power edge applications. With Arm Cortex-M cores and integrated hardware accelerators, STM32 MCUs support TinyML and real-time AI inferencing while maintaining energy efficiency. The STM32 MCUs are designed for smart sensing, industrial automation, and battery-powered IoT devices.
Synaptics Astra SL Series
The Astra SL series is designed for AI-driven smart cameras, wearables, and industrial IoT devices. It utilizes heterogeneous computing, which dynamically allocates tasks to the most power-efficient processors (NPUs, DSPs, or MCUs).
This approach ensures that AI workloads are processed with minimal energy consumption. Its ability to balance energy efficiency with high performance is crucial for battery-powered devices that need to operate for extended periods without frequent recharging.
Building Edge AI Models
AI models are designed to meet the limited power and resource constraints of edge devices and process data in real-time with high accuracy. Below are some of the techniques used to build edge AI models.
Model Compression and Simplification
To ensure AI models fit within the memory and power limitations of edge devices, they are often compressed using techniques like quantization (reducing the precision of calculations) and pruning (removing redundant or unnecessary neural network connections). These methods reduce memory usage and the computational load, resulting in models that consume less power while maintaining sufficient accuracy for edge applications.
Efficient Architecture Design
Traditional AI models with dense layers and high computational demands are typically too resource-intensive for edge deployment. Instead, lightweight architectures are optimized for energy efficiency. For instance, MobileNet is commonly used for image classification, and TinyBERT is designed for natural language processing tasks. These models are crafted to perform well on devices with limited resources, offering high computational efficiency while reducing power consumption.
Pre-Trained Models
Pre-trained models like those in ModelNova can be fine-tuned for specific edge applications. These models are already trained on large datasets and are ready for deployment, reducing the need for extensive model development and the associated computational costs. Fine-tuning these pre-trained models ensures they can quickly adapt to specific tasks while requiring fewer resources.
Challenges in Edge AI Development
While edge AI offers significant advantages in sustaining AI technology, some challenges need to be addressed for broader adoption and optimal performance.
Hardware Limitations
Many edge devices still struggle with processing power, memory, and storage capacity compared to centralized data centers. This limits their ability to handle complex AI models, especially those requiring high computational resources. To support more advanced AI applications, manufacturers must develop more specialized chipsets that balance power efficiency with performance. Overcoming these limitations will be critical to enabling more sophisticated AI models at the edge.
Managing Large-Scale Edge Networks
The decentralized nature of edge computing means that businesses often need to manage vast networks of devices. Coordinating these devices and ensuring seamless communication across such a large infrastructure can be challenging, especially now edge devices are multiplying and becoming more integrated into IoT ecosystems. Ensuring that devices can work together efficiently while maintaining local processing capabilities will be an ongoing challenge for scaling edge AI solutions.
Security Concerns
With data being processed on numerous edge devices rather than centralized locations, the risk of security breaches increases. Edge devices often have varying levels of protection, making them attractive targets for cyberattacks. Companies must invest in robust security measures such as end-to-end encryption, secure booting, and regular updates to protect these devices from vulnerabilities.
embedUR’s Role in Sustainable Edge AI
embedUR is playing a crucial role in accelerating the adoption of AI at the edge. Our collaboration with silicon vendors allows us to integrate ModelNova, our suite of pre-optimized AI models, into chipsets by developing firmware and SDKs specifically designed for their chips.
ModelNova covers many use cases, including computer vision, speech recognition, and natural language processing. These models are optimized for low-power, high-performance edge devices so businesses can deploy AI applications with minimal energy consumption.
By embedding these pre-trained models directly into chipsets, we enable businesses to launch edge AI solutions faster and more efficiently, even without deep expertise in AI. This seamless integration reduces the need for extensive customization or optimization, which makes it easier to scale products and bring them to market quickly.
For silicon vendors, offering chips that come with an ecosystem of ready-to-use models will sell faster. A chip with a built-in AI model ecosystem is far more appealing to customers, as it allows them to skip the development phase and get up and running immediately.
Through our efforts, embedUR is helping to drive the future of sustainable edge AI by enabling faster, more efficient deployment of AI applications. If you enjoyed this post, check out how AI is transforming the agricultural industry.
