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DISCO: Distributed Inference with Sparse Communications
For example, each layer of a ResNet-50 model can be distributively inferred across two nodes with five times less data communications, almost half overall computations and half memory requirement for a single node, and achieve comparable accuracy to the original ResNet-50 model.
Noisy Computations during Inference: Harmful or Helpful?
During inference, by injecting proper noise to signals in the neural networks, the robustness of adversarially-trained neural networks against black-box attacks has been further enhanced by 0. 5% and 1. 13% for two adversarially trained models for MNIST and CIFAR10, respectively.
Training Recurrent Neural Networks against Noisy Computations during Inference
One of the motivations for looking into this problem is to reduce the high power cost of conventional computing of neural network operations through the use of analog neuromorphic circuits.
