Standard deep learning models are bloated for edge hardware. Quantization is the primary weapon for shrinking models without losing their soul.
1From FP32 to INT8
Most neural networks are trained using FP32 (32-bit Floating Point) numbers. While precise, these numbers take up significant memory and require complex floating-point hardware to compute. Quantization is the process of mapping these continuous values into a discrete set of lower-precision values, usually INT8 (8-bit Integer). By reducing the number of bits per weight from 32 to 8, we achieve a 4x reduction in model size. More importantly, integer operations are typically faster and consume less energy on edge devices, enabling real-time performance on batteries.
Weight_FP32: 0.7412984...
Weight_INT8: 95
Memory_Reduction: 75%
Status: COMPRESSION_ACTIVE2PTQ vs QAT Strategies
There are two paths to a quantized model. Post-Training Quantization (PTQ) is fast; you take a finished model and 'round' the weights. This is easy but can significantly hurt accuracy in small models. Quantization-Aware Training (QAT) is the gold standard. During training, the model 'knows' it will be quantized and learns to be robust against the rounding errors. This preserves nearly all of the original FP32 accuracy while delivering the memory benefits of INT8. Choosing the right strategy depends on your accuracy requirements and available training time.
Mode: QAT
Training: SIMULATED_PRECISION_LOSS
Accuracy: 99.1%
Status: HIGH_PRECISION_TINY_MODEL