The Tensorflow Model Optimization Toolkit minimizes the complexity of optimizing inference. Inference efficiency is a critical issue when deploying machine learning models to mobile devices because of the model size, latency, and power consumption.
Computational demand for training grows with the number of models trained on different architectures, whereas the computational demand for inference grows in proportion to the number of users.
Model optimization is useful for:
- Deploying models to edge devices with restrictions on processing, memory, or power-consumption. For example, mobile and Internet of Things (IoT) devices.
- Reduce the payload size for over-the-air model updates.
- Execution on hardware constrained by fixed-point operations.
- Optimize models for special purpose hardware accelerators.
Model optimization uses multiple techniques:
- Reduce parameter count with pruning and structured pruning.
- Reduce representational precision with quantization.
- Update the original model topology to a more efficient one with reduced parameters or faster execution. For example, tensor decomposition methods and distillation.
We support quantization, and are working to add support for other techniques.
Quantizing deep neural networks uses techniques that allow for reduced precision representations of weights and, optionally, activations for both storage and computation. Quantization provides several benefits:
- Support on existing CPU platforms.
- Quantization of activations reduces memory access costs for reading and storing intermediate activations.
- Many CPU and hardware accelerator implementations provide SIMD instruction capabilities, which are especially beneficial for quantization.
TensorFlow Lite provides several levels of support for quantization.
- Post-training quantization quantizes weights and activations post training and is very easy to use.
- Quantization-aware training allows for training networks that can be quantized with minimal accuracy drop and is only available for a subset of convolutional neural network architectures.
Latency and accuracy results
Below are the latency and accuracy results for post-training quantization and quantization-aware training on a few models. All latency numbers are measured on Pixel 2 devices using a single big core. As the toolkit improves, so will the numbers here:
|Model||Top-1 Accuracy (Original)||Top-1 Accuracy (Post Training Quantized)||Top-1 Accuracy (Quantization Aware Training)||Latency (Original) (ms)||Latency (Post Training Quantized) (ms)||Latency (Quantization Aware Training) (ms)||Size (Original) (MB)||Size (Optimized) (MB)|
Choice of quantization tool
As a starting point, check if the models in hosted models can work for your application. If not, we recommend that users start with the post-training quantization tool since this is broadly applicable and does not require training data. For cases where the accuracy and latency targets are not met, or hardware accelerator support is important, quantization-aware training is the better option.