How edge AI can power real-time decision making | Kisaco Research
Edge AI

How edge AI can power real-time decision making

With data generation expected to reach 175 zettabytes by 2025, the demand for real-time processing has never been bigger and is expected to grow. Globally, the edge artificial intelligence (AI) market was valued at $20 billion in 2023 and is projected to grow to $186 billion by 2032, due to the fact that edge AI enables near-instant data processing and analysis.

Instead of sending data back and forth to a cloud server, which takes time, edge AI cuts out the middleman and does everything within the device itself. This means users get lightning-fast insights that enable split-second decisions.

As smart factories take off, it’s likely that pretty soon an average manufacturing company will control more than 50,000 internet of things (IoT) sensors and generate several petabytes of data per day. That’s not to mention all the other use cases, such as medical devices, retail, autonomous vehicles and others.
What is edge AI?

Edge AI is AI that runs at the "edge" of a network. Instead of sending data to faraway cloud servers for analysis, edge AI processes everything right on the spot. It sorts the data and analyzes it using local devices such as smartphones, IoT devices, or factory machines.

For example, a factory sensor could monitor machine performance and detect potential malfunctions before they lead to breakdowns. Edge AI enables these devices to process and analyze this data locally, making real-time decisions to enhance efficiency and prevent downtime.

Edge AI’s ability to make automatic split-second decisions is useful for some applications, such as factory robots and emergency medical devices. Cloud AI, while powerful, simply isn’t fast enough for real-time responsiveness. For instance, robots in factories, warehouses, or those used for last-mile delivery can leverage vision-based analytics for tasks such as detecting obstacles, estimating the position and orientation of objects (e.g., pose estimation).

Surveillance cameras enhanced with edge AI capabilities can perform advanced functions including detecting objects automatically, recognizing intrusions and potential hazards, and monitoring for safety purposes.

Edge AI devices use advanced built-in processing power. Typical edge AI hardware includes:

-GPUs: e.g., NVIDIA Jetson, AMD Radeon Instinct MI200
-TPUs: e.g., Google Edge TPU
-Custom ASICs: e.g., CSEM, Marvell

Edge AI chips are designed to handle the complex calculations required by AI quickly and efficiently. For instance, TPUs crunch the large matrices that are the backbone of machine learning algorithms.

Training and inference of edge AI devices

Edge AI devices operate through two key phases: training and inference.

Training phase

Initially, the models are trained in the cloud on large datasets. This refines their algorithms based on known outcomes, enhancing their accuracy. The process adjusts the model to improve its performance and once the optimization is complete, the model is streamlined or "compressed" to function efficiently on less powerful, smaller edge devices.

Inference phase

After the training phase, the edge device executes the AI algorithms using embedded software that supports specific AI tasks. The software, operating on platforms like TensorFlow Lite and PyTorch Mobile, manages the functioning of the AI directly on the device using an edge TPU. These frameworks are optimized for mobile and edge environments, allowing for agile and efficient task execution.

Real-time analysis and autonomous operation

In practical terms, once deployed, edge AI devices perform data processing locally and in real-time.

For instance, edge AI devices can process and react to sensory data from their surroundings immediately — like a camera in an autonomous vehicle recognizing stop signs and prompting the vehicle to stop. Similarly, a smartphone might adjust its battery usage based on how it's being used, thanks to the local processing capabilities of edge AI.

Despite the autonomy edge AI provides, these devices still connect to the cloud periodically to update AI models with new data, download software updates, or offload non-time-sensitive data.

Benefits of using edge AI vs. cloud AI

1. Faster data processing

The faster a system gathers data, analyzes it and provides insights or makes decisions, the more efficient it is. For instance, a Google Edge TPI performs up to 4 trillion operations per second (4 TOPS) while only consuming 2 W of power, which makes it extremely fast and energy efficient.

Using dedicated AI accelerators, whether discrete or integrated into a system-on-chip (SoC), improves the throughput and reduces the latency of AI applications. These accelerators are optimized for the entire vision pipeline, including video pre- and post-processing and neural network inferencing.

When used in factories, edge AI detects machine breakdowns immediately, alerting managers to fix them, potentially saving hours of downtime. It also helps with inventory management, allowing for rapid reorders to meet customer demands and avoid lost sales.

2. Enhanced data security

The further data travels, the higher the risk of it being intercepted or tampered with. Edge AI minimizes data transit, speeds up threat detection and prevents data loss.

Local processing helps you comply with data handling regulations more easily, building trust with your customers or users. Keeping data local also aligns with data sovereignty laws, which may require data to be stored and processed within specific geographic boundaries.

3. Decreased operational costs

By moving data processing to local devices, the load on cloud services is greatly reduced. This saves money in several ways.

Sending data to and from the cloud gets expensive, especially with large amounts of data like in industries that use video surveillance or IoT setups. Processing this data locally with edge AI means much less data is sent over the internet. This lowers the need for bandwidth and cuts down on data transmission spending.

Cloud services usually bill you based on how much data you store and how much computing power you use. When you process more of your data on local devices, you use less cloud storage and fewer cloud resources. This means your monthly bills from your cloud service provider should be lower.

Processing data locally is more energy-efficient than cloud services. Edge AI reduces energy costs and is better for the environment. Setting up edge AI has upfront costs, such as buying new hardware. But over the long run, maintaining and upgrading a local system is often cheaper than a cloud-based system. You can make upgrades piece by piece, as needed, rather than overhauling everything at once.

4. Remote and offline reliability

In places with poor or unreliable connectivity, such as remote oil rigs, rural farms or even areas with inconsistent urban coverage, edge AI devices can still work well. They process data locally, which means critical operations keep running even if the internet goes down.

For example, in farming, smart edge AI devices monitor soil and crop conditions in real-time, managing irrigation and fertilization automatically. This is useful in big, rural fields where internet access is limited.

5. Scalability

Edge AI systems are modular and flexible, which means you can start with just a few devices in key areas and expand as needed. This approach lets you expand your AI use without big initial infrastructure costs.

For example, a factory might first use edge AI on essential production lines to spot defects. As needs grow, they could extend this to all production lines and include more tasks such as inventory management and logistics. This way, they increase their capacity without overwhelming their central systems.

Source: Electronics360. Read the full article here