Hardware Considerations
Implementing AI at the edge requires selecting hardware suitable for resource-constrained environments, unlike cloud servers offering unlimited computational power. Edge devices like microcontrollers, FPGAs, and NPUs operate under strict limitations in power, processing capacity, memory, and sometimes physical size.
Microcontrollers are cost-effective for edge AI but have limited memory and processing power. For example, the Arduino Uno, a popular microcontroller, has only 32 KB of flash memory and 2 KB of RAM. FPGAs balance flexibility and performance but are more expensive and power-hungry. They are often used in applications requiring high-speed data processing, such as image and signal processing. NPUs, specialized for deep learning tasks, require more power and are typically found in advanced edge devices like smartphones and autonomous vehicles. Examples of NPUs include Google’s Tensor Processing Unit (TPU) and Intel’s Movidius Myriad X.
Devices in remote or power-sensitive environments should prioritize energy efficiency, while applications requiring fast real-time decision-making may benefit from additional computational power. The choice of hardware depends on the specific requirements of the AI model and the edge environment.
Model Optimization Techniques
Given the hardware constraints of edge devices, AI models must be optimized to run efficiently without sacrificing accuracy. Model quantization, such as converting 32-bit floating-point numbers to 8-bit integers, reduces the model’s size, leading to faster inference times and lower memory consumption. This is crucial for edge devices with limited memory.
Quantization can be achieved through techniques like post-training quantization, where the model is quantized after training, or quantization-aware training, where the model is trained with quantization in mind.
Model pruning involves removing less important connections within a neural network to reduce complexity without significantly impacting performance. This results in faster inference and lower power consumption, which is crucial for edge AI applications. Pruning can be done by removing individual weights or entire neurons based on their contribution to the model’s accuracy.
Knowledge distillation trains a smaller, more efficient “student” model based on a larger, pre-trained “teacher” model, making it suitable for resource-constrained environments like edge devices.
The “student” model learns to mimic the “teacher” model’s behavior, achieving comparable accuracy with a smaller footprint.
Software Frameworks
Several software frameworks have been developed to deploy AI models on resource-constrained devices. One widely used framework is TensorFlow Lite Micro, designed for microcontrollers and low-power devices. It allows running pre-trained models on devices with minimal RAM, ideal for sensor data analysis, speech recognition, and simple computer vision tasks on embedded systems.
For instance, TensorFlow Lite Micro can be used to deploy a keyword spotting model on a microcontroller to enable voice control for a smart appliance.
Another important tool is CMSIS-NN, a neural network library optimized for ARM Cortex-M processors commonly used in edge devices. It provides efficient neural network kernels to minimize memory usage and computational load for AI inference tasks on microcontrollers.
CMSIS-NN can be used to accelerate the inference of image classification models on a microcontroller-based security camera.
UTensor offers a lightweight, open-source framework for more complex applications to deploy AI models on resource-constrained devices, specifically microcontrollers. Developed in partnership with ARM, the sensor integrates seamlessly with TensorFlow, enabling developers to convert models for execution on embedded systems.
UTensor can be used to deploy a gesture recognition model on a wearable device to enable intuitive user interactions.