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InterTrain: Accelerating DNN Training using Input Interpolation
This is because the efficacy of learning on interpolated inputs is reduced by the interference between the forward/backward propagation of their constituent inputs.
Accelerating DNN Training through Selective Localized Learning
The trend in the weight updates made to the transition layer across epochs is used to determine how the boundary betweenSGD and localized updates is shifted in future epochs.
Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks
We also evaluate the impact of the attack on a sparsity-optimized DNN accelerator and demonstrate degradations up to 1. 59x in latency, and also study the performance of the attack on a sparsity-optimized general-purpose processor.
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