Quantization is the process of mapping continuous, high-precision numerical values (like 32-bit floats) to a smaller, discrete set of lower-precision values (like 8-bit integers). Primarily used in AI and signal processing, it reduces model size, speeds up inference, and lowers power consumption with minimal loss in accuracy.
IBM
IBM
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Key Aspects of Quantization:
How it Works: It compresses data by converting complex floating-point weights and activations into integers (e.g., FP32 to INT8 or INT4).
AI/LLM Benefits: Quantization makes large models (LLMs) smaller and faster, allowing them to run on edge devices like mobile phones, while reducing memory usage and operational costs.
Types:
Post-Training Quantization (PTQ): Applied after a model is trained to reduce size.
Quantization-Aware Training (QAT): Models the loss of precision during training to improve accuracy.
Trade-off: While it improves efficiency, reducing precision can lead to a slight decrease in model accuracy.
Other Applications: Beyond AI, it is used in digital signal processing (e.g., converting audio/images to digital) and music production to align MIDI notes to a timing grid
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