Deploying Keras models on edge devices or mobile platforms requires optimizing both the model architecture and the task of inference. Here are some of the techniques for optimizing the deployment of Keras models on edge devices or mobile platforms-
1. Quantization: Quantization is the process of converting the model weights and activations from float32 to int8 values, which reduces the model size and speeds up computation. The smaller model size in turn reduces the memory requirements for deployment on edge devices. There are several types of quantization, such as dynamic range quantization (DRQ), post-training quantization, and quantization-aware training (QAT). The kind of quantization depends on the model and hardware configuration.
2. Pruning: Pruning a model involves removing unnecessary weights, inputs, or layers. This can reduce the size of the model, as well as speed up inference time by reducing unnecessary computations. Some researchers have proposed using a combination of pruning and quantization to reduce the size of the model even more.
3. Model architecture optimization: When deploying Keras models on edge devices or mobile platforms, it’s essential to optimize the model architecture for the device’s hardware. For example, mobile GPUs may have a limit on the number of operations they can perform, so models should be designed with smaller layer sizes and fewer layers. Similarly, models with 1x1 convolutional layers rather than dense layers can be more hardware-friendly.
4. On-device inference: Moving inference computations to the edge device instead of sending data to the server can reduce bandwidth and make inference faster. In some cases, this may require porting portions of the model’s architecture to the device’s hardware or using specialized hardware like TPUs to speed up computation.
5. Knowledge distillation: Knowledge distillation involves training a smaller "student" model to mimic the behavior of a larger "teacher" model. This technique can be useful when deploying Keras models on edge devices as the smaller model has fewer parameters and can run faster on hardware with limited resources.
In conclusion, optimizing the deployment of Keras models on edge devices or mobile platforms requires a combination of techniques ranging from model architecture optimization to hardware-aware post-training quantization, and from on-device inference to knowledge distillation. By combining the right techniques, developers can create efficient and fast models that deliver excellent results on edge devices and mobile platforms.