What Is Edge Inference and Why It’s a Game Changer for AI

Imagine your smart devices, like drones or security cameras, detecting and classifying objects independently, without a cloud connection. This would reduce network latency, allowing devices to make accurate decisions in real time, and enhance privacy by ensuring sensitive data never leaves the device. Edge inference, or AI inference at the edge, makes this possible by letting devices deploy and run high-performance AI models locally. This blog will illustrate its significance and provide practical guidance on deploying edge inference to achieve your goals. Additionally, understanding AI Inference vs Training is crucial to optimizing model performance and ensuring smooth deployment.

One helpful resource for getting started with edge inference is AI inference APIs. These valuable tools can help you seamlessly deploy high-performance AI models at the edge to achieve real-time processing, lower costs, and enhanced privacy without relying on the cloud.

What Is Edge Inference, and Why Is It So Important?

Edge AI, or AI on the edge, merges edge computing and artificial intelligence to run machine learning tasks directly on interconnected edge devices. By enabling data storage close to the device’s location, edge computing minimizes reliance on cloud processing.

AI algorithms then analyze this data at the network’s edge, functioning with or without an internet connection. This setup allows for millisecond-level processing, ensuring real-time feedback for users.

Key Industry Applications of Edge AI: Enhancing Efficiency and Security

Technologies such as self-driving cars, wearable devices, security cameras, and smart home appliances leverage Edge AI to deliver instant, critical information. As industries continue to explore its potential, Edge AI is gaining traction for optimizing workflows, automating business processes, and driving innovation—while simultaneously addressing challenges like:

• Latency
• Security
• Cost efficiency

Edge AI vs. Distributed AI: What’s the Difference?

Edge AI enables localized decision-making, reducing the need to transmit data to a central location and wait for processing. This facilitates the automation of business operations. Data must still be transmitted to the cloud to retrain AI models and deploy updates. Scaling this approach across multiple locations and diverse applications presents challenges such as:

• Data gravity
• System heterogeneity
• Scalability
• Resource constraints

The Role of Multi-Agent Systems in Distributed AI

Distributed AI (DAI) helps address these challenges by enabling intelligent data collection, automating AI life cycles, adapting and monitoring edge devices, and optimizing data and AI pipelines. DAI coordinates and distributes tasks, objectives, and decision-making processes within a multi-agent environment, allowing AI algorithms to operate autonomously across:

• Multiple systems
• Domains
• Edge devices at scale

Edge AI vs. Cloud AI: What’s the Difference?

Cloud computing and APIs are primarily used to train and deploy machine learning models. Edge AI enables machine learning tasks such as predictive analytics, speech recognition, and anomaly detection to be performed closer to the user.

Rather than relying solely on cloud-based applications, edge AI processes and analyzes data near its source. This allows machine learning algorithms to run directly on IoT devices, eliminating the need to transmit data to a private data center or cloud computing facility.

Enhancing Real-Time Decision-Making in Autonomous Systems with Edge AI

Edge AI is particularly beneficial when real-time predictions and data processing are critical. Rapid decision-making is essential for safe navigation in self-driving vehicles, which must instantly detect and respond to various factors, including:

• Traffic signals
• Erratic drivers
• Lane changes
• Pedestrians
• Curbs

By processing data locally within the vehicle, edge AI reduces the risk of delays caused by connectivity issues when sending data to a remote server. In high-stakes situations where immediate response times can be a matter of life or death, edge AI ensures the vehicle reacts swiftly and effectively.

Scalability and Performance Benefits of Cloud AI for Advanced Model Training

Cloud AI refers to deploying AI models on cloud servers, providing enhanced data storage and processing power. This approach is ideal for training and deploying complex AI models that require significant computational resources.

Key Differences Between Edge AI and Cloud AI

Computing Power

Cloud AI can provide better computational capabilities and storage capacity than edge AI, facilitating the training and deploying of more intricate and advanced AI models. Edge AI has a limit on processing capacity due to the device’s size limitation.

Latency

Latency directly affects:

• Productivity
• Collaboration
• Application performance
• User experience

The higher the latency (and the slower the response times), the more these areas suffer. Edge AI provides reduced latency by processing data directly on the device, whereas cloud AI sends data to distant servers, leading to increased latency.

Network Bandwidth

Bandwidth refers to the public data transfer of inbound and outbound network traffic around the globe. Edge AI calls for lower bandwidth due to local data processing on the device, whereas cloud AI involves data transmission to distant servers, demanding higher network bandwidth.

Security

Edge architecture offers enhanced privacy by processing sensitive data directly on the device, whereas cloud AI entails transmitting data to external servers, potentially exposing sensitive information to third-party servers.

Benefits of Edge AI for End Users

According to a Grand View Research, Inc. report, the global edge AI market was valued at USD 14,787.5 million in 2022 and is expected to grow to USD 66.47 million by 2023.

This rapid expansion of edge computing is driven by the rise in demand for IoT-based edge computing services, alongside edge AI’s other inherent advantages.

The primary benefits of edge AI include:

Diminished Latency

Through complete on-device processing, users can experience rapid response intervals without any delays caused by the need for information to travel back from a distant server.

Decreased Bandwidth

As edge AI processes data locally, it minimizes the data transmitted over the Internet, preserving internet bandwidth. The data connection can handle more simultaneous data transmission and reception with less bandwidth.

Real-Time Analytics

Users can perform real-time data processing on devices without system connectivity and integration, enabling them to save time by consolidating data without communicating with other physical locations.

Edge AI might encounter limitations in managing the extensive volume and diversity of data demanded by specific AI applications. It may need to be integrated with cloud computing to harness its resources and capacities.

Data Privacy

Privacy increases because data isn’t transferred to another network, which may be vulnerable to cyberattacks. By processing information locally on the device, edge AI reduces the risk of data mishandling.

In industries subject to data sovereignty regulations, edge AI can aid in maintaining compliance by locally processing and storing data within designated jurisdictions. On the other hand, any centralized database has the potential to become an enticing target for potential attackers, meaning edge AI isn’t completely immune to security risks.

Scalability

Edge AI expands systems using cloud-based platforms and inherent edge capabilities on original equipment manufacturer (OEM) technologies, encompassing software and hardware.

These OEM companies have begun to integrate native edge capabilities into their equipment, simplifying the process of scaling the system. This expansion also enables local networks to maintain functionality even when nodes upstream or downstream experience downtime.

Reduced Costs

Expenses associated with AI services hosted on the cloud can be high. Edge AI offers the option of utilizing costly cloud resources as a repository for post-processing data accumulation intended for subsequent analysis rather than immediate field operations. This reduces the workloads of cloud computers and networks.

CPU, GPU, and memory utilization significantly reduces as their workloads are distributed among edge devices, distinguishing edge AI as the more cost-effective option.

Reducing Network Congestion and Enhancing Efficiency with Edge Computing

When cloud computing handles all the computations for a service, the centralized location bears a significant workload. Networks endure high traffic to transmit data to the central source. As machines execute tasks, the networks become active once more, transmitting data back to the user.

Edge devices remove this continuous back-and-forth data transfer. As a result, both networks and machines experience reduced stress when they’re relieved from the burden of handling every aspect.

Cost Efficiency and Reduced Human Oversight in Edge AI Implementation

The autonomous traits of edge AI eliminate the need for continuous supervision by data scientists. Although human interpretation will consistently play a pivotal role in determining the ultimate value of data and the outcomes that it yields, edge AI platforms assume some of this responsibility, ultimately leading to cost savings for businesses.

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What’s the Difference Between Data Center/Cloud vs. Edge Inference?

Cloud inference is the original method for running inference on AI models. With cloud inference, the model runs on a server in a data center, and the results are returned to the user.

Edge inference, on the other hand, runs the model locally on an edge device. The results are returned instantly because there is no need to send data anywhere and no latency associated with waiting for a response.

What's Under the Hood?

Cloud-based AI inference initially relied on CPUs, notably Intel’s Xeon processors. However, as AI models grew in complexity, the industry shifted toward more efficient architectures, with data centers adopting specialized accelerators like Nvidia GPUs to enhance inference performance. With their multiple cores and high multiply-accumulate (MAC) operations per clock cycle, these GPUs significantly reduce processing time for large AI models.

Data centers optimize inference by running multiple AI jobs simultaneously and batching them to boost efficiency. Their advanced cooling systems support high-power PCIe boards with thermal design power (TDP) ratings ranging from 75 to 300 watts. Inference accelerators can handle various AI models, continuously scaling performance to accommodate increasingly complex workloads.

How Does Edge Inference Work?

Running inference at the edge is very different. Edge systems typically run one model from one sensor. The sensors capture some portion of the electromagnetic spectrum, such as light, radar, or LIDAR, in a 2D “image” of 0.5 to 6 megapixels. The sensors capture data at frame rates from 10 to 100 frames per second.

Applications are almost always latency sensitive; the customer wants to process the neural network model as soon as frames are captured to take action. So, customers wish to batch sizes equal to one. Batching from one sensor means waiting to accumulate 2, 4, or 8 images before processing them; latency is terrible. Many applications are accuracy-critical. Think medical imaging, for example. You want your X-ray or Ultrasound diagnosis to be accurate!

Optimization of Edge AI Models: Convolutional Architectures and Power-Efficient Hardware for Cost-Effective Performance

The models are typically convolution-intensive and often derivatives of YOLOv3. Some edge systems incorporate small servers (think MRI machines, which are big and expensive) and can handle 75W PCIe cards.

Many edge servers are lower-cost and can benefit from less costly PCIe cards with good price/performance. Higher-volume edge systems incorporate inference accelerator chips that dissipate up to 15W (no fans).

Cloud Inference vs. Edge Inference: Use Cases

The application everyone thinks of first is typically autonomous vehicles. But actual autonomous driving is a decade or more away. In the 2020s, the value of inference will be in driver assistance and safety (detecting distraction, sleep, etc). Design cycles are 4-5 years, so a new inference chip today won’t show up in your vehicle till 2025 or later. What are the other markets using edge inference today?

Edge Servers

Last year, Nvidia announced inference sales outstripped training for the first time. This was likely shipped to data centers, but many applications are outside. This means that sales of PCIe inference boards for edge inference applications are likely in the hundreds of millions of dollars per year and are rapidly growing. Many edge servers are deployed in factories, hospitals, retail stores, financial institutions, and other enterprises. In many cases, sensors in the form of cameras are already connected to the servers, but they record what’s happening in case of an accident or theft. Now, these servers can be supercharged with low-cost PCIe inference boards.

Cost-Effective Edge AI Inference: Affordable Hardware Solutions and Applications in Diverse Industries

There are many applications: surveillance, facial recognition, retail analytics, genomics/gene sequencing, industrial inspection, medical imaging, and more. Since training in floating point and quantization requires a lot of skill/investment, most edge server inference is likely done in 16-bit floating point, with only the highest-volume applications being done in INT8.

Until now, edge servers that did inference used the Nvidia Tesla T4, a great product but $2000+. Many servers are low-cost and can now benefit from inference accelerator PCIe boards at prices as low as $399, but with the throughput/$ being the same or better than T4.

Higher Volume Edge Systems

Higher volume, high accuracy/quality imaging applications include:

• Robotics
• Industrial automation/inspection
• Medical imaging
• Scientific imaging
• Cameras for surveillance
• Object recognition
• Photonics, etc.

In these applications, the end products sell for thousands to millions of dollars, the sensors capture 0.5 to 6 Megapixels, and “getting it right” is critical, so they want to use the best models (for example, YOLOv3, which is a heavy model at 62 million weights and >300 billion MACs to process a 2 megapixel image) and to use the most prominent image size they can (just like humans, we can recognize people better with a large crisp image than a small one).

Scaling Edge AI: The Need for Higher Throughput and Efficiency in Low-Cost Inference Solutions

The leading players here are Nvidia Jetson (Nano, TX2, Xavier AGX, and Xavier NX) at 5- 30W and $250-$800. Customers we talk to are starved for throughput and are looking for solutions that will give them more throughput and larger image sizes for the same power/price as they use today.

Their solutions will be more accurate and reliable when they get it, and market adoption and expansion will accelerate. So, although the applications today are in the thousands or tens of thousands of units, this will proliferate with the availability of inference that delivers more and more throughput/$ and throughput/watt.

Cost-Effective Inference Accelerators: Driving High-Volume Edge AI Applications

Some inference accelerators can outperform Xavier NX at lower power and at prices for million/year quantities that are 1/10th of Xavier NX. This will drive much higher-volume applications of performance inference acceleration. This market segment should become the largest because of the breadth of applications.

Low Accuracy/Quality Imaging

Many consumer products or applications where accuracy is nice but not critical will opt for tiny images and simpler models like Tiny YOLO. In this space, the leaders are Jetson Nano, Intel Movidius, and Google Edge TPU at $50-$100.

Voice and Lower Throughput Inference

Imaging neural network models require trillions of MACs/second for 30 frames/second of megapixel images. For keyword recognition, voice processing requires billions of MACs/second or even less.

These applications, like Amazon Echo, are already significant in adoption and volume, but the $/chip is much less. The players in this market differ from those in the above market segments.

Cell Phones

Almost all cell phone application processors have an AI module of the SoC for local processing of simple neural network models. The leading players here are:

• Apple
• Qualcomm
• Mediatek
• Samsung

This is the highest unit volume of AI deployment at the edge today.

What Matters for Edge Inference Customers

Latency

The first is latency. Edge systems make decisions based on images of up to 60 frames per second. In a car, for example, it is vital that objects like people, bikes, and vehicles be detected and their presence acted upon in as little time as possible. In all edge applications, latency is #1, which means batch size is almost always 1.

Numerics

The second is numerics. Many edge server customers will stay with floating point for a long time, and BF16 is the easiest for them to move to since they just truncate 16 bits off their FP32 inputs and weights.

Given the cost and complexity of quantization, fanless systems will be INT8 if they are high volume, but many will be BF16 if volumes stay in the thousands. An inference accelerator that can do both gives customers the ability to start quickly with BF16 and shift seamlessly to INT8 when they are ready to invest in quantization.

Throughput

The third is throughput for the customer’s model and image size. Customers typically run one model and know their image size and sensor frame rate. Almost every application wants to process megapixel images (1, 2, or 4) at 30 or even 60 frames/second frame rates.

Most applications are vision CNNs, but many have many different models, even ones processing 3-dimensional images or images in time (think MRI, etc.), LIDAR, or financial modeling. The only customers who run more than one model are automotive, which must process vision, LIDAR, and one or two other models simultaneously.

Efficency

Fourth is efficiency. Almost all customers want more throughput/image size per dollar and watt. Most tell us they want to increase throughput and image size for their current dollar and power budgets. As throughput/$ and throughput/watt increase, new applications will become possible at the low end of the market, where the volumes are exponentially larger.

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Edge Inference Concepts and Architecture Consideration

Edge computing is about processing real-time data near the data source, which is considered the network’s edge. Applications are run as physically close as possible to the site where the data is being generated instead of a centralized cloud or data center storage location.

For example, suppose a vehicle automatically calculates fuel consumption based on data received directly from the sensors. In that case, the computer performing that action is called an Edge Computing device or simply “Edge device”.

Data Processing

• Edge Computing: Processes data closer to the source, minimizing the need for data transfer.
• Cloud Computing: Stores and processes data in a central location, typically a data center.

Latency

• Edge Computing: Significantly reduces latency, enabling near-instant inference and decreasing network lag-related failures.
• Cloud Computing: Requires more time to process data, as it involves data transfer between the edge and the cloud.

Security and Privacy

• Edge Computing: Keeps most data localized, reducing system vulnerabilities.
• Cloud Computing: Has a larger attack surface, making it more susceptible to security threats.

Power Efficiency and Cost

• Edge Computing: Utilizes accelerators to reduce both cost and power consumption per inference channel.
• Cloud Computing: Incurs higher expenses due to connectivity, data migration, bandwidth, and latency considerations.

Enhancing Security and Efficiency with On-Device AI Inference

The integration of Artificial Intelligence (AI) algorithms in edge computing enables an edge device to infer or predict based on the continuously received data, known as Inference at the Edge. Inference at the edge allows data-gathering devices, such as sensors, cameras, and microphones, to provide actionable intelligence using AI techniques.

It also improves security as the data is not transferred to the cloud. Inference requires a pre-trained deep neural network model. Typical tools for training neural network models include:

• Tensorflow
• MxNet
• Pytorch
• Caffe

The model is trained by feeding as many data points as possible into a framework to increase its prediction accuracy.

Architecture Considerations: Building a Foundation for Edge Inference

Throughput

For images, measuring the throughput in inferences/second or samples/second is a good metric because it indicates peak performance and efficiency. These metrics are often found in benchmark tools such as ResNet-50 or MLPerf. Knowing the required throughput for the use cases in the market segments helps determine the processors and applications in your design.

Latency

This is a critical parameter in edge inference applications, especially in manufacturing and autonomous driving, where real-time applications are necessary. Images or events that happen need to be processed and responded to within milliseconds.

Hardware and software framework architectures influence system latency. Understanding the system architecture and choosing the proper SW framework are essential.

Precision

High precision, such as 32-bit or 64-bit floating-point, is often used for neural network training to achieve a sure accuracy faster when processing large data sets. This complex operation usually requires dedicated resources due to the high processing power and extensive memory utilization. Inference, in contrast, can achieve a similar accuracy by using lower-precision multiplications because the process is more straightforward, optimized, and compressed data.

Inference does not require as much processing power and memory utilization as training; resources can be shared with other operations to reduce cost and power consumption. Using cores optimized for the different precision levels of matrix multiplication also helps to increase throughput, reduce power, and increase the overall platform efficiency.

Power consumption

Power is critical when choosing processors, power management, memory, and hardware devices to design your solution. Some edge inference solutions, such as safety surveillance systems or portable medical devices, use batteries to power the system.

Power consumption also determines the thermal design of the system. A design that can operate without additional cooling components such as a fan or heatsink can lower the product cost.

Design scalability

Design scalability expands a system for future market needs without redesigning or reconfiguring it. This also includes the effort of deploying the solution in multiple places.

Most edge inference solution providers use heterogeneous systems that can be written in different languages and run on various operating systems and processors. Packaging your code and all its dependencies into container images can also help you deploy your application quickly and reliably to any platform, regardless of the location.

Use case requirements

Understanding how your customers use the solution determines the features your solution should support. The following are some examples of use case requirements for different market segments.

Industrial/Manufacturing

• High-sensitivity cameras for low-light ambient, smoke, sparks, heat, and splattered hazards.
• Requires multiple installation points.
• Small cameras with 360-degree views at places where humans can't access.

Retail

• Multiple camera connections to the edge computers.
• Real-time object detection and triggering system.
• Cameras with a 360-degree view of shelves and POS monitoring.
• Easy integration with existing systems, such as POS and RFID systems.

Medical/Healthcare

• Rechargeable battery-powered systems for mobility.
• A real-time image capturing with high resolution.
• Accelerators are required to run complex calculations.

Smart city

• Ruggedized system to withstand extreme weather like fog, snow, and thunderstorms.
• Must be able to operate 24/7. Able to detect objects in low light ambient such as in a tunnel or under a bridge.
• Able to integrate with smoke, fire, or falling object detection.
• Hardware needs to be able to operate in an extended temperature range to match outdoor conditions.
• The neural network model training dataset should include different ambient, weather, and season.

Start Building with $10 in Free API Credits Today!

AI Inference powers the smooth operation of AI applications. It reduces the vast models produced during AI training to a size that can be easily managed and provides the capability to generate rapid predictions using these smaller models.

With AI inference, businesses can continuously improve the performance and accuracy of their AI applications while lowering costs. The more a model is run, the better it gets.

Standard Inference vs. Specialized Inference

OpenAI-compatible serverless inference APIs allow developers to run large language models (LLMs) with minimal upfront costs. Inference offers the highest performance at the lowest price on the market and provides specialized batch processing for large-scale asynchronous AI workloads.

Beyond standard inference, Inference also provides document extraction capabilities designed explicitly for retrieval-augmented generation applications.

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