Edging out the cloud?
_{Running AI Algorithms and models directly on Edge devices}

Combining the power of AI with edge computing, Edge AI brings intelligence and decision-making capabilities directly to the edge of the network, enabling faster, more efficient, and privacy-preserving applications. However, this comes with a new set of challenges, most notably how to optimize AI models so that they can run on these low powered edge devices.

In this blog, we explore what Edge AI is, how to use model compression techniques to address hardware limitations and some current and future applications of the approach.

Understanding Edge AI

Edge AI refers to the deployment of AI algorithms and models directly on edge devices, such as smartphones, Internet of Things (IoT) devices, and smart sensors, rather than relying on cloud-based data centers.

This decentralized approach eliminates the need to send all data to the cloud for processing, making AI-driven applications more responsive, sustainable, and privacy-friendly. This approach also makes solutions more tolerant to slow or patchy connectivity.

Key Benefits of Edge AI

Privacy and Security: Edge AI helps safeguard user privacy by processing data locally, without sending it to external servers. This approach ensures that sensitive information, like facial images or biometric data, remains on the device and isn’t exposed during data transmission.

Sustainability: By processing data on the edge, Edge AI reduces the amount of raw data that needs to be sent to the cloud. Only relevant and processed information is transmitted, reducing the need to transmit and store the raw data, avoiding the associated costs and energy impact. Compressing models ensures they are efficient and use a minimal amount of energy to run. 

Real-time Decision Making: With Edge AI, devices can make intelligent decisions locally, without relying on cloud connectivity. This is particularly valuable for applications that need rapid responses or do not have assured network connections.

The need for Model Compression

Edge devices are, however, typically much slower than their data center counterparts, often needing to meet restrictive power and thermal constraints. Traditional deep learning models often contain millions or even billions of parameters, which require substantial computational power and memory to process.

Edge AI models, therefore, must be optimized to run well on the target hardware. Model compression is an excellent way to do this. Model compression describes a set of techniques aimed at reducing the size of deep learning models, without compromising their performance.

Model Compression techniques

There are multiple techniques that can be employed individually or in combination to reduce model size and computational complexity. Some examples are:

• Pruning: Pruning involves removing unnecessary connections and nodes from the neural network. This reduces the size of the network and required computation at run time, though there is a limit to the amount a network can be pruned before the impact on results becomes significant.
• Quantization: Quantization reduces the precision of model parameters, typically from 32-bit floating-point numbers to lower bit representations (often 8-bit). This reduces memory and computation requirements, without significantly compromising accuracy.
• Hardware optimization: Models can be tuned to take advantage of specific hardware features that accelerate inferencing, such as graphics processing units (GPUs) and tensor processing units (TPUs).

Note: If you also need to preserve data privacy while training AI models, you need to look at approaches like Federated Learning, as covered here.

Applications of Edge AI

• Healthcare: Wearable health devices can utilize Edge AI to analyze and interpret real-time health data, alerting users or healthcare providers to potential issues, while ensuring potentially sensitive patient data remains on device.
• Industrial IoT: In manufacturing and industrial settings, Edge AI can power predictive maintenance algorithms, optimizing production processes and reducing downtime.
• Agriculture: Edge AI-powered sensors in agriculture can monitor crop health, optimize irrigation schedules, and detect anomalies, without requiring constant internet access.

Edge AI represents a transformative shift in AI deployment, empowering devices at the edge of the network with intelligence and decision-making capabilities. By harnessing the advantages of edge computing, Edge AI overcomes the limitations of traditional cloud-based AI and opens up a world of possibilities for real-time, privacy-preserving, and efficient applications.

Looking to the future, the growth of 5G networks and advancements in hardware capabilities will further propel Edge AI’s adoption. The combination of low-latency connectivity and powerful edge devices will unlock new possibilities for AI applications in areas we’ve only begun to explore.