In the world of rapidly evolving artificial intelligence, efficiency and scalability bear as much importance as accuracy. That is where AI quantisation comes in-a game-changing technique that brings down model size and computational requirements while yielding stellar performance. From enabling AI on resource-constrained devices like smartphones to driving real-time applications in edge computing, quantisation is bringing a sea change in the way AI models are designed and deployed. Let’s dive into how this powerful approach optimises efficiency, accelerates computation, and extends the possibilities with AI for a wide range of industries.
What is AI Quantisation
AI quantisation reduces the precision of numerical values in models to make them smaller, faster, and more efficient, without greatly affecting accuracy. By mapping continuous data to discrete values, it enables AI to run smoothly on resource-limited devices like smartphones and IoT systems. Techniques such as uniform and non-uniform quantization handle data differently to balance simplicity and flexibility, while methods like dynamic range and full-integer quantization focus on reducing size and improving speed. Advanced approaches like Quantisation-Aware Training (QAT) improve accuracy during training, and Post-Training Quantisation (PTQ) simplifies implementation after training. Innovations like LoRA and QLoRA combine quantization with fine-tuning for adaptable models, making them ideal for edge computing and energy-efficient applications..
Why Use AI Quantisation
Quantisation reduces model size, accelerates computations, and lowers power consumption by using low-precision integer operations. This makes it perfect for resource-limited devices like mobile or embedded systems. While accuracy may slightly decrease due to reduced precision, strategies like dynamic quantization and weight sharing help minimise the trade-off, enabling efficient AI deployment without significantly compromising performance.
Here are 4 applications of Quantisation in AI
- Edge Computing: Quantisation enables AI models to perform efficiently on edge devices like smartphones, IoT devices, and embedded systems where the computational resources are minimal.
- Reduced Latency: Lowering the computational load through quantisation has helped drive faster inference times, which is critical for real-time applications.
- Efficiency: The low precision of computations decreases energy consumption for AI models and makes these models even more affordable to operate in environments that have scarce energy.
- Storage Efficiency and Memory: Quantised models have lower storage requirements, allowing us to deploy larger models for low-memory devices.
AI quantisation is much more than a technical step forward; it’s a harbinger of advanced AI access and efficiency in an array of applications. Be it real-time insights on IoT devices, reduced latency in edge computing, or efficient energy use to enable sustainable deployment, quantization is laying the pathway toward faster and much smarter AI that readily adapts to a dynamically changing environment. Ready to harness the power of quantisation for your AI needs? Reach out to us at specialists@edenai.co.za.
This post was enhanced using information from:
Rover, J. (2024) Understanding Quantization in AI: A Comprehensive Guide Including LoRA and QLoRA
https://dev.to/jackrover/understanding-quantization-in-ai-a-comprehensive-guide-including-lora-and-qlora-4dl1
Gen. David. L. (2023) Introduction to AI Model Quantization Formats
https://medium.com/@tubelwj/introduction-to-ai-model-quantization-formats-dc643bfc335c