The Power of Edge Inference for Faster, Smarter Decision-Making

As machine learning gains traction across industries, businesses need a way for machine learning deployment in real time to make AI-driven decision-making faster and more efficient. In a practical sense, this means getting predictions from machine learning models after training on data relevant to a specific task. The faster you can get these predictions, the quicker you'll be able to identify any defects and take action to mitigate costly errors. Edge inference helps with this goal by bringing inference processes closer to the data source to reduce latency and improve operational efficiency. This post will explore how edge inference works, its benefits, and how to start today.

AI inference APIs are valuable tools that can help you achieve your objectives, such as faster, more innovative, and more efficient AI-driven decision-making through the power of edge inference.

What is Edge Inference?

Edge computing is about processing real-time data near the data source, which is considered the network's edge. Applications are run as close 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 "Edge device."

AI-Powered Inference in Edge Computing

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 is a technique that enables data-gathering from devices to provide actionable intelligence using AI techniques rather than relying solely on cloud-based servers or data centers.

Deploying Edge Servers for Low-Latency AI Inference

It involves installing an edge server with an integrated AI accelerator (or a dedicated AI gateway device) close to the source of data, which results in much faster response time. This technique improves performance by reducing the time from input data to inference insight, and reduces the dependency on network connectivity, ultimately improving the business bottom line.

Inference at the edge improves security as the large dataset does not have to be transferred to the cloud.

Edge Inference for Real-Time Insights and Enhanced Security

Inference at the edge enables 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 Consideration

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 happened need to be processed and responded to within milliseconds. Hardware architecture and software framework architecture influence the system latency.

Therefore, understanding the system architecture and choosing the proper SW framework is essential.

Precision

High precision, such as 32-bit or 64-bit floating-point, is often used for neural network training to achieve higher accuracy faster when processing a large dataset. This complex operation usually requires dedicated resources due to the high processing power and extensive memory utilization.

Lower-Precision Inference for Resource Optimization

Inference, in contrast, can achieve a similar accuracy by using lower-precision multiplications because the process is more straightforward, optimized, and compressed. Inference does not need as much processing power and memory utilization as training. Therefore, 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:

• Increase throughput
• Reduce power
• Improve the platform's overall 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 heat sink, 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.

The Components of Edge Inference

Edge inference requires a pre-trained deep learning model, which is usually built on a framework like:

• TensorFlow
• MXNet
• PyTorch

A Three-Step Process for Deploying AI Inference

First, an engineer trains the model on a standard server by inputting large amounts of data to increase accuracy. Next, the model is optimized for edge deployment, reducing its size to run on resource-constrained devices. The model is deployed on an edge device, where it can make accurate predictions on new data in real-time.

Types of Edge Inference

Edge inference can occur differently, depending on the application and available resources. Here are the three main types of edge inference:

On-device Inference

This occurs when models run directly on edge devices like a smart camera or industrial sensor. On-device inference offers complete data privacy, eliminates network latency, and enables real-time processing. The main drawback is that edge devices have limited computational resources, which can restrict the complexity of machine learning models.

Edge Server Inference

This occurs when models run on nearby edge servers rather than on the devices collecting the data. Also called “edge cloud inference,” this approach reduces latency and increases computational power, allowing for more complex models. Nevertheless, there is still some partial data exposure when using this method.

Cooperative inference

This occurs when multiple devices collaborate to run AI models. Cooperative inference distributes the computational load across the devices, similar to peer-to-peer networks. This approach can enhance privacy through data sharding, as not all devices need a complete model copy to make accurate predictions.

Related Reading

• CPU Acceleration
• AI Workloads

Edge Inference Applications and Use Cases

Real-Time Processing: The Need for Speed

One of the most significant advantages of AI inference at the edge is the ability to process data in real time. Traditional cloud computing often involves sending data to centralized servers for analysis, which can introduce latency due to distance and network congestion. Edge computing mitigates this by processing data locally on edge devices or near the data source.

This low-latency processing is crucial for applications requiring immediate responses, such as:

• Autonomous vehicles
• Industrial automation
• Healthcare monitoring

Privacy and Security: Keeping Data Local

Transmitting sensitive data to cloud servers for processing poses potential security risks. Edge computing addresses this concern by keeping data close to its source, reducing the need for extensive data transmission over potentially vulnerable networks.

This localized processing enhances data privacy and security, making edge AI particularly valuable in sectors handling sensitive information, such as finance, healthcare, and defense.

Bandwidth Efficiency: Less is More

By processing data locally, edge computing significantly reduces the volume of data that needs to be transmitted to remote cloud servers. This reduction in data transmission requirements has several vital implications, including reducing network congestion, as the local processing at the edge minimizes the burden on network infrastructure.

The Cost-Saving Advantage of Edge Inference

Secondly, the diminished need for extensive data transmission leads to lower bandwidth costs for organizations and end-users, as transmitting less data over the Internet or cellular networks can translate into substantial savings. This benefit is particularly relevant in environments with limited or expensive connectivity, such as remote locations.

In essence, edge computing optimizes the utilization of available bandwidth, enhancing the system's overall efficiency and performance.

Scalability: Smart Growth

AI systems at the edge can be scaled efficiently by deploying additional edge devices as needed, without overburdening central infrastructure. This decentralized approach also enhances system resilience. In network disruptions or server outages, edge devices can continue to operate and make decisions independently, ensuring uninterrupted service.

Energy Efficiency: Power to the Edge

Edge devices are often designed to be energy-efficient, making them suitable for environments where power consumption is a critical concern. By performing AI inference locally, these devices minimize the need for energy-intensive data transmission to distant servers, contributing to overall energy savings.

Hardware Accelerators: Powering Edge Inference

AI accelerators, such as NPUs, GPUs, TPUs, and custom ASICs, enable efficient AI inference at the edge. These specialized processors are designed to handle the intensive computational tasks AI models require, delivering high performance while optimizing power consumption.

Integrating accelerators into edge devices makes it possible to run complex deep learning models in real time with minimal latency, even on resource-constrained hardware. This is one of the best AI enablers, allowing larger and more powerful models to be deployed at the edge.

Offline Operation: The Edge Doesn’t Need the Cloud

Offline operation through Edge AI in IoT is critical, particularly when constant internet connectivity is uncertain. Edge AI systems ensure uninterrupted functionality in remote or inaccessible environments with unreliable network access.

This resilience extends to mission-critical applications, enhancing response times and reducing latency, such as in autonomous vehicles or security systems.

Edge AI devices can locally store and log data when connectivity is lost, safeguarding data integrity. Furthermore, they are integral to redundancy and fail-safe strategies, providing continuity and decision-making capabilities, even when primary systems are compromised.

This capability augments the adaptability and dependability of IoT applications across a broad spectrum of operational settings.

Customization and Personalization: Tailoring Intelligence at the Edge

AI inference at the edge enables high customization and personalization by processing data locally. Systems can deploy real-time customized models for user needs and specific environmental contexts. AI systems can quickly respond to changes in user behavior, preferences, or surroundings, offering highly tailored services.

The ability to customize AI inference services at the edge without relying on continuous cloud communication ensures faster, more relevant responses, enhancing user satisfaction and overall system efficiency.

The traditional paradigm of centralized computation, wherein these models reside and operate exclusively within data centers, has limitations, particularly in scenarios where real-time processing, low latency, privacy preservation, and network bandwidth conservation are critical.

Meeting the Edge Demand

This demand for AI models to process data in real time while ensuring privacy and efficiency has led to a paradigm shift for AI inference at the edge. AI researchers have developed various optimization techniques to improve the efficiency of AI models, enabling AI model deployment and efficient inference at the edge.

Use Cases: AI Inference at the Edge

The rapid advancements in artificial intelligence (AI) have transformed numerous sectors, including:

• Healthcare
• Finance
• Manufacturing

AI models, intense learning models, have proven highly effective in tasks such as:

• Image classification
• Natural language understanding
• Reinforcement learning

Performing data analysis directly on edge devices is becoming increasingly crucial in scenarios like:

• Augmented reality
• Video conferencing
• Streaming
• Gaming
• Content Delivery Networks (CDNs)
• Autonomous driving
• Industrial Internet of Things (IoT)
• Intelligent power grids
• Remote surgery
• Security-focused applications, where localized processing is essential.

Internet of Things (IoT): A Smart Approach to Sensor Data

The capabilities of smart sensors significantly drive the expansion of the Internet of Things (IoT). These sensors act as the primary data collectors for IoT, producing large volumes of information. Nevertheless, centralizing this data for processing can result in delays and privacy issues. This is where edge AI inference becomes crucial.

The Evolution of Smart Sensors Through Edge AI

AI models facilitate immediate analysis and decision-making right at the source by integrating intelligence directly into smart sensors. This localized processing reduces latency and the necessity of sending large data quantities to central servers. As a result, smart sensors evolve from mere data collectors to real-time analysts, becoming essential in the progress of IoT.

Industrial Applications: Improving Processes with Edge AI

In industrial sectors, especially manufacturing, predictive maintenance is crucial in identifying potential faults and anomalies in processes before they occur. Traditionally, heartbeat signals, which reflect the health of sensors and machinery, are collected and sent to centralized cloud systems for AI analysis to predict faults.

Nevertheless, the current trend is shifting. By leveraging AI models for data processing at the edge, we can enhance the system's performance and efficiency, delivering timely insights at a significantly reduced cost.

Mobile / Augmented Reality (AR): Edge AI Reduces Latency

In mobile and augmented reality, the processing requirements are significant due to the need to handle large volumes of data from various sources such as cameras, Lidar, and multiple video and audio inputs. To deliver a seamless augmented reality experience, this data must be processed within a stringent latency range of about 15 to 20 milliseconds.

Synergistic Integration

AI models are effectively utilized through specialized processors and cutting-edge communication technologies. The integration of edge AI with mobile and augmented reality results in a practical combination that enhances real-time analysis and operational autonomy at the edge.

This integration reduces latency and aids in energy efficiency, which is crucial for these rapidly evolving technologies.

Security Systems: Improving Threat Detection

Combining video cameras with edge AI-powered video analytics in security systems transforms threat detection. Traditionally, video data from multiple cameras is transmitted to cloud servers for AI analysis, which can introduce delays. With AI processing at the edge, video analytics can be conducted directly within the cameras.

Fortifying Critical Infrastructure

This allows for immediate threat detection, and depending on the analysis's urgency, the camera can quickly notify authorities, reducing the chance of threats going unnoticed. This move to AI-integrated security cameras improves response efficiency and strengthens security at crucial locations such as airports.

Robotic Surgery: Improving Safety and Operations

In critical medical situations, remote robotic surgery involves conducting surgical procedures with the guidance of a surgeon from a remote location. AI-driven models enhance these robotic systems, allowing them to perform precise surgical tasks while maintaining continuous communication and direction from a distant medical professional.

Ensuring Safety and Reliability

This capability is crucial in the healthcare sector, where real-time processing and responsiveness are essential for smooth operations under high-stress conditions. Deploying AI inference at the edge is vital for such applications to ensure safety, reliability, and fail-safe operation in critical scenarios.

Autonomous Driving: The Role of Edge AI in Self-Driving Cars

Autonomous driving is a pinnacle of technological progress, with AI inference at the edge taking a central role. AI accelerators in cars empower vehicles with onboard models for rapid, real-time decision-making. This immediate analysis enables autonomous cars to navigate complex scenarios with minimal latency, bolstering safety and operational efficiency.

Edge AI Driving Adaptability and Safety in Self-Driving Cars

By integrating AI at the edge, self-driving cars adapt to dynamic environments, ensuring safer roads and reduced reliance on external networks. This fusion represents a transformative shift, where vehicles become intelligent entities capable of swift, localized decision-making, ushering in a new era of transportation innovation.

Related Reading

• AI Cloud Computing
• Edge AI vs. Cloud AI
• GPU vs. CPU for AI

Edge Inferencing by Example

To illustrate how inferencing works, we use TensorFlow as our deep learning framework. TensorFlow is an open-source deep learning framework developed by the Google Brain team. It is widely used for building and training ML models, especially those based on neural networks. The following example illustrates how to create a deep learning model in TensorFlow.

Leveraging TensorFlow Lite and Google Edge TPU

The model classifies images into separate categories, for example, sea, forest, or building. We can create an optimized version of that TensorFlow Lite model with post-training quantization. The edge inferencing uses TensorFlow-Lite as the underlying framework and Google Edge Tensor Processing Unit (TPU) as the edge device.

This process involves the following steps:

• Create the model
• Train the model
• Save the model
• Apply post-training quantization
• Convert the model to TensorFlow Lite
• Compile the TensorFlow Lite model using an edge TPU compiler for Edge TPU devices like the Coral Dev board (a Google development platform that includes the Edge TPU) to a TPU USB Accelerator (this allows users to add Edge TPU capabilities to existing hardware by simply plugging in the USB device).
• Deploy the model at the edge to make inferences.

The Challenges of Edge Inference

Limited Computational Resources

Edge devices often have less processing power and memory compared to cloud servers. This may limit the complexity and size of AI models deployed at the edge.

Model Optimization

AI models may need to be optimized and compressed to run efficiently on resource-constrained edge devices while maintaining acceptable accuracy.

Model Updates

Updating AI models at the edge can be more challenging than in a centralized cloud environment, as devices might be distributed across various locations and may have varying configurations.

The Operational Challenges of Edge Inference

Handling a deep learning process involves continuous data pipeline management and infrastructure management. This leads to the following questions:

• How do I manage the acquisition of the models to the edge platform of the models?
• How do I stage the model?
• How do I update the model?
• Do I have sufficient computational and network resources for the AI inference to execute properly?
• How do I manage the model's drift and security (privacy protection and adversarial attack)?
• How do I manage the inference pipelines, insight pipelines, and datasets associated with the models?

Related Reading

• Pros and Cons of Serverless Architecture
• Edge AI Examples

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