I'm trying to deploy a Computer Vision model on a mobile device, but the latency is over 500ms. I've heard about Quantization and Pruning, but I'm worried about losing too much mAP (Mean Average Precision). Which optimization technique offers the best "speed vs accuracy" trade-off?
3 answers
For mobile, 8-bit Quantization (INT8) is usually the "sweet spot." It can reduce your model size by 4x and speed up inference significantly with only a 1-2% drop in accuracy. If you use "Quantization-Aware Training" (QAT) instead of post-training quantization, the model actually learns to be accurate despite the lower precision. Pruning is also great, but it requires specialized hardware to actually see the speed benefits—standard CPUs don't always handle "sparse matrices" well. I’d start with INT8 quantization and then look into Knowledge Distillation if you still need more speed.
Have you tried using specialized runtimes like ONNX or TensorFlow Lite? Sometimes the bottleneck isn't the model itself, but how the operations are being executed on the mobile GPU.
Start with a "Mobile-First" architecture like MobileNet or Tiny-YOLO. It's much easier than trying to shrink a massive ResNet or Transformer down to fit on a phone.
Great point, Jennifer. Designing for the hardware from day one is always better than trying to optimize an oversized model later.
David is right. Moving from a standard Python environment to a C++ based runtime like TFLite or CoreML can cut your latency in half without even changing the model weights. These runtimes are highly optimized for the specific instruction sets of mobile chips (like ARM's NEON). Also, make sure you are utilizing the NPU (Neural Processing Unit) if the device has one. Many developers accidentally run their models on the CPU, which is much slower for tensor operations.