Testing Modern Applications with Embedded AI Features (Part-1)

Embedded AI involves integrating artificial intelligence directly into devices, sensors, or systems at the edge of a network. Unlike traditional cloud-based AI, which relies on powerful servers, embedded AI operates locally on less powerful hardware. This setup enables real-time decision-making, reduces delays, and enhances privacy by keeping data processing on-site. 

In this article, we explore key challenges in deploying AI on resource-constrained devices, methods for deploying AI on embedded devices, discuss hardware acceleration techniques, and examine current application models of embedded AI. 

Key Challenges in Deploying AI on Resource-Constrained Devices 

Deploying AI on resource-constrained devices presents several challenges. These devices often have limitations in memory, processing power, and energy capacity, requiring careful optimisation of AI models to ensure efficient performance within these constraints. Tailoring AI algorithms to fit available resources is essential but can be complex. 

Moreover, applications such as autonomous vehicles and industrial automation systems demand immediate responses. Meeting these real-time requirements while ensuring prompt execution of AI algorithms can be particularly challenging. Additionally, embedded AI must function effectively across a range of hardware platforms, from small microcontrollers to powerful edge servers, which adds to the complexity of deployment and integration. 

A crucial aspect of embedded AI is its intersection with edge computing and the Internet of Things (IoT). By processing data locally on smart devices, wearables, and industrial sensors, embedded AI reduces the need for centralised cloud services. This local processing enhances efficiency, security, and scalability in our increasingly connected world. 

Methods for Deploying AI on Embedded Devices 

Deploying AI on resource-constrained embedded devices requires several techniques to make models efficient and practical. Two key methods are quantization and pruning. Quantization reduces the precision of model weights and activations by using 8-bit integers or even binary values instead of the traditional 32-bit floating-point numbers. This reduces the model’s memory usage and computational needs. Pruning, on the other hand, removes unnecessary connections or weights from neural networks, making them sparser and reducing their size and computational demands. For instance, the Lottery Ticket Hypothesis shows that significant pruning can be done while preserving model performance. 

Another effective method is knowledge distillation, where a large, complex model, known as the teacher, transfers its knowledge to a smaller, more efficient model, called the student. The student model learns from the teacher’s predictions and ends up being compact yet effective. An example is BERT, a large language model, being distilled into DistilBERT, a smaller model with minimal loss in performance. 

Quantization-aware training is also crucial. This technique ensures that the model is trained with the awareness that weights will be quantized during deployment. This approach helps maintain accuracy even after the model is compressed. TensorFlow’s tf.lite framework is an example of a tool that supports this type of training. 

When it comes to neural network compression, sparse neural networks offer benefits by having fewer active connections, which saves memory and speeds up inference. Recent studies indicate that these sparse networks can achieve similar accuracy to dense ones. Additionally, architectures like MobileNet and EfficientNet are designed specifically for mobile and embedded devices. MobileNet uses depth-wise separable convolutions to reduce computation while maintaining accuracy, and EfficientNet scales the model’s depth, width, and resolution to balance accuracy and efficiency. 

Lastly, edge AI accelerators like Google’s Edge TPU and NVIDIA Jetson are hardware solutions designed to enhance neural network execution on edge devices. These accelerators offload computations from the CPU or GPU, boosting inference speed and energy efficiency, making them crucial for deploying AI on embedded devices. 

Hardware Acceleration Techniques for Embedded Devices 

Convolutional accelerators are specialised hardware designed to boost the performance of convolutional neural networks (CNNs), which are commonly used for tasks like image and video processing. These accelerators significantly speed up key operations such as convolutions, pooling, and activation functions. Popular examples include Graphics Processing Units (GPUs) and Neural Processing Units (NPUs). GPUs are known for their ability to handle parallel processing tasks effectively, while NPUs are optimised specifically for neural network computations. NPUs, in particular, have become increasingly popular due to their efficiency in managing CNNs for image recognition and video processing. 

Load distribution strategies are crucial for optimising AI workloads on embedded systems. These strategies help manage how computations are distributed across different hardware components. For instance, model partitioning involves breaking a neural network model into smaller segments that can be processed by different accelerators. Task offloading refers to assigning specific tasks, such as inference or training, to various hardware components to balance the load. Dynamic scheduling adjusts workload distribution based on real-time conditions, ensuring that resources are used efficiently. 

For example, in an edge device analysing traffic camera footage, the CNN used for object detection might run on an NPU, while a simpler rule-based algorithm handles traffic light detection. Recent research focuses on dynamic scheduling techniques to better balance power consumption with performance in edge AI systems, aiming to enhance efficiency and effectiveness in real-time applications. 

Real-World Applications of Embedded AI 

Smartphones and Wearables benefit greatly from embedded AI. In smartphones, AI personalises user experiences by tailoring app recommendations, improving camera performance, and optimising battery life. Wearables, such as fitness trackers and smartwatches, use embedded AI to monitor health metrics like activity levels, heart rate, and sleep patterns, providing users with valuable insights into their well-being. 

In the realm of smart homes and cities, embedded AI plays a significant role. For home automation, AI manages smart devices like lights, thermostats, and security systems, enhancing energy efficiency and user convenience. In smart cities, AI helps optimise traffic flow, reduce congestion, and improve safety by analysing and managing traffic data. 

Industrial automation also relies on embedded AI to boost efficiency. Predictive maintenance uses AI to forecast equipment failures, minimising downtime and cutting maintenance costs. During manufacturing, AI helps with real-time quality control by detecting defects, and it analyses machinery data to optimise processes and reduce waste. 

Autonomous vehicles showcase the advanced capabilities of embedded AI. Self-driving cars process data from sensors like lidar, radar, and cameras to navigate, avoid obstacles, and make decisions. Similarly, drones use embedded AI for autonomous flight, obstacle detection, and tracking, enhancing their functionality and safety. 

In entertainment and content recommendations, embedded AI customises user experiences. Streaming services use AI to suggest content based on individual preferences, while gaming consoles utilise AI to enhance graphics, adapt gameplay, and optimise performance, providing a richer and more engaging experience. 

The Future of Embedded AI: Challenges & Solutions

Embedded AI is revolutionising how we interact with technology by bringing intelligence directly to devices, enhancing performance, privacy, and efficiency. We’ve explored methods for deploying AI on constrained devices, the role of hardware accelerators, and various real-world applications from smartphones to autonomous vehicles. 

In part two of this series, we’ll dive into the challenges of testing embedded AI. We’ll cover testing frameworks and tools, data generation and validation, and best practices to ensure robust and reliable AI systems. 

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Key Takeaways 

Embedded AI Overview: Integrates AI directly into devices, sensors, or systems at the network edge, enabling real-time decision-making and enhancing privacy by processing data locally. 

Deployment Challenges: 

• Resource Constraints: Devices often have limited memory, processing power, and energy capacity. 

• Real-Time Requirements: Ensuring immediate responses in applications like autonomous vehicles and industrial automation. 

• Hardware Diversity: AI must work across varied platforms from microcontrollers to edge servers. 

• Edge Computing & IoT: Local data processing reduces reliance on central cloud services, improving efficiency and scalability. 

Deployment Methods: 

• Quantization & Pruning: Reduce model size and computational needs. 

• Knowledge Distillation: Transfers knowledge from a large model (teacher) to a smaller one (student). 

• Quantization-Aware Training: Ensures model accuracy post-compression. 

• Sparse Neural Networks: Achieve efficiency with fewer active connections. 

• MobileNet & EfficientNet: Optimised architectures for mobile and embedded devices. 

• Edge AI Accelerators: Specialised hardware like Google’s Edge TPU and NVIDIA Jetson boost performance and efficiency. 

Hardware Acceleration: 

• Convolutional Accelerators: GPUs and NPUs enhance CNN performance for tasks like image and video processing. 

• Load Distribution: Strategies like model partitioning, task offloading, and dynamic scheduling manage computation across hardware. 

Real-World Applications: 

• Smartphones & Wearables: Personalise experiences and monitor health metrics. 

• Smart Homes & Cities: Manage devices for automation and optimise traffic flow. 

• Industrial Automation: Predict equipment failures, ensure quality control, and optimise processes. 

• Autonomous Vehicles: Handle navigation and obstacle detection. 

• Entertainment: Customise content recommendations and enhance gaming experiences. 

Future Outlook: The next article will explore testing challenges, frameworks, tools, data generation, and best practices for robust embedded AI systems.