Introduction

As artificial intelligence (AI) quickly moves through industries, deploying AI models in devices (for example, IoT sensors, drones, medical instruments, and personal mobile devices) is becoming commonplace. Edge and embedded systems are often constrained in memory, compute, and energy.
This is the essential question: How do we deploy high-performing AI models on resources-constrained devices?

The answer lies in AI model compression techniques–a series of methodologies that have been developed to decrease the size and computing resource demand of AI models without sacrificing performance. For organization who are looking to train and equip teams with capability to deploy AI at the edge, knowledge of these methodologies will be beneficial.

This blog outlines the key model compression techniques and how corporate training with clear objectives and outcomes can be constructed and delivered to develop the technical capabilities of teams to deploy AI in embedded environments.

Why Compress AI Models?

Modern deep learning algorithms (e.g., GPT, ResNet, or BERT) can have millions (or even billions!) of parameters. They require extensive GPU resources, large memory footprints, and high energy consumption—conditions that cannot be satisfied with embedded devices.

Model compression can help organizations:

• Reduce inference latency
• Curb battery consumption
• Reduce memory and storage requirements
• Facilitate real-time decisions at the edge
• Reduce reliance on cloud infrastructure

This is especially critical in industries like healthcare, defense, manufacturing, and automotive, where real-time processing and data privacy are paramount.

Core Techniques in AI Model Compression

Let’s explore the four primary techniques used to compress AI models:

1. Quantization
Quantization decreases the precision of the numbers that represent model parameters and computations. The model may use 8 bits (INT8) to represent the values in place of 32-bit floating-point (FP32) or up to binary.

· Post-Training Quantization (PTQ): this method quantizes weights after training.

· Quantization-Aware Training (QAT): Time spent in training will simulate quantized behavior, target accuracy is increased.

2. Pruning
Pruning eliminates unnecessary weights or neurons from a network. Think of it like trimming branches from a tree—keeping only what’s essential.

·       Unstructured Pruning: Removes individual weights.

·       Structured Pruning: Removes entire neurons, channels, or layers.

Both methods result in sparse models that are smaller and faster.

3. Knowledge Distillation

This involves training a smaller model (the “student”) to replicate the behavior of a larger model (the “teacher”). The student learns not just the final output but also the teacher’s soft labels and internal representations.

Benefits:

·       Smaller model with comparable accuracy

·       Faster inference

4. Neural Architecture Search (NAS) and Efficient Models

Some models are built for efficiency from the beginning— MobileNet, SqueezeNet and EfficientNet are good examples. NAS provides an automatable process to find effective architectures that optimize size, speed, and accuracy.

Structuring a Corporate Training Program

To best deploy model compression in a company, training must be pragmatic for a role, and scalable. Here are steps to consider:

1. Audience Segmentation

Split the learners into categories based on their roles:

·       ML Engineers/Data Scientists: Focus on model design and training.

·       Embedded Developers: Emphasize deployment and optimization.

· Product Managers: Provide conceptual understanding and ROI-driven perspectives.

2. Hands-On, Project-Based Learning

Theory is crucial, but nothing replaces experience. Incorporate real-world projects like:

·       Deploying a quantized object detection model on a Raspberry Pi.

·       Comparing latency and accuracy trade-offs of a pruned model vs. a full model.

·       Implementing distillation for a customer service chatbot on an ARM Cortex-M processor.

3. Tool Familiarity

Familiarize teams with the tools used for model compression and deployment:

·       TensorFlow Lite & TFLite Micro

·       ONNX Runtime

·       PyTorch Mobile

·       NVIDIA TensorRT

·       Apache TVM

·       Edge Impulse (for non-coders)

These tools support quantization, pruning, and deployment to various edge devices.

4. Performance Metrics and Testing

Teach teams to evaluate:

·       Model size

·       Inference latency

·       Accuracy drop (pre- and post-compression)

·       Energy consumption

Encourage use of benchmarking tools and hardware simulators to test before deployment.

5. Security and Privacy Considerations

In edge AI deployments, privacy and security can’t be an afterthought. Training should cover:

·       On-device inference benefits for data privacy

·       Risks of model inversion attacks on compressed models

·       Best practices for secure model storage and updates

Case Studies for Corporate Learning

1. Healthcare Wearables
   Compression enabled a hospital to deploy a patient monitoring AI model on a smartwatch. By applying quantization and pruning, they reduced inference time by 60% and power consumption by 45%, without sacrificing detection accuracy for anomalies.

2. Industrial IoT (IIoT)
   A manufacturing firm used distillation to deploy predictive maintenance models on factory-floor edge devices. This allowed for real-time fault detection without relying on the cloud.

For the organization wanting to continue the momentum in a fast-paced development environment, corporate training program in embedded AI and model compression is a sound move to invest their engineering and developer training in proper skills and tools to allow organizations to spur innovation, reduce capital infrastructure costs, and deploy AI in a smarter, more agile and sustainable manner with edge devices.
As all devices become smart and intelligent, organizations capable of managing ‘robotic AI’ will lead us into the next generation of digital transformation.