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AUTOSPARSE: Towards Automated Sparse Training of Deep Neural Networks
We integrated GA with the latest learnable pruning methods to create an automated sparse training algorithm called AutoSparse, which achieves better accuracy and/or training/inference FLOPS reduction than existing learnable pruning methods for sparse ResNet50 and MobileNetV1 on ImageNet-1K: AutoSparse achieves (2x, 7x) reduction in (training, inference) FLOPS for ResNet50 on ImageNet at 80% sparsity.
K-TanH: Efficient TanH For Deep Learning
We propose K-TanH, a novel, highly accurate, hardware efficient approximation of popular activation function TanH for Deep Learning.
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