Synthetic Gradients

Definition: Synthetic Gradients (SGs) are a method used in deep learning to decouple layers in a neural network during training. They provide an approximation of the true gradient, allowing each layer to update its parameters independently, without waiting for the backpropagation of gradients from subsequent layers. This technique can significantly speed up the training process and improve the efficiency of neural networks.

Explanation: In traditional deep learning models, the backpropagation algorithm is used to compute gradients, which are then used to update the model’s parameters. This process is sequential, meaning that each layer must wait for the layers that follow it to compute their gradients before it can update its own parameters. This dependency can slow down the training process, especially in deep networks.

Synthetic Gradients aim to solve this problem by allowing each layer to update its parameters independently. This is achieved by training a separate model, often referred to as a ‘gradient predictor’, for each layer. The gradient predictor’s job is to predict the gradient that would be computed by backpropagation, based on the layer’s input and output. Once trained, these gradient predictors can provide an approximation of the true gradient almost instantly, allowing the layer to update its parameters without waiting for the rest of the network.

Benefits: The main benefit of Synthetic Gradients is that they can significantly speed up the training process. By decoupling the layers, they allow for parallel updates, which can be particularly beneficial in deep networks or when using hardware that supports parallel computation. Additionally, because the gradient predictors are trained to approximate the true gradient based on the layer’s input and output, they can potentially provide a more accurate gradient than backpropagation, especially in cases where the true gradient is difficult to compute.

Drawbacks: Despite their benefits, Synthetic Gradients also have some drawbacks. The main one is that they require additional computation to train the gradient predictors. This can increase the complexity of the model and the amount of memory required. Additionally, while the gradient predictors can provide a good approximation of the true gradient, they are not always perfect. This can lead to less accurate updates and potentially slower convergence.

Use Cases: Synthetic Gradients can be used in any deep learning model where the speed of training is a concern. They are particularly useful in deep networks, where the sequential nature of backpropagation can significantly slow down training. They can also be beneficial in cases where the true gradient is difficult to compute, as the gradient predictors can provide a good approximation based on the layer’s input and output.

Related Terms: Backpropagation, Deep Learning, Neural Networks, Gradient Descent, Parallel Computation

Further Reading:

• Decoupled Neural Interfaces using Synthetic Gradients
• Understanding Synthetic Gradients and Decoupled Neural Interfaces

References:

1. Jaderberg, M., Czarnecki, W. M., Osindero, S., Vinyals, O., Graves, A., & Kavukcuoglu, K. (2016). Decoupled Neural Interfaces using Synthetic Gradients. arXiv preprint arXiv:1608.05343.
2. Prakash, A. (2017). Understanding Synthetic Gradients and Decoupled Neural Interfaces. Medium.