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Found 5 papers, 0 papers with code
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.
Ramanujan Bipartite Graph Products for Efficient Block Sparse Neural Networks
We also propose to use products of Ramanujan graphs which gives the best connectivity for a given level of sparsity.
A Study of BFLOAT16 for Deep Learning Training
In this paper, we discuss the flow of tensors and various key operations in mixed precision training, and delve into details of operations, such as the rounding modes for converting FP32 tensors to BFLOAT16.
Hierarchical Block Sparse Neural Networks
In this work, we jointly address both accuracy and performance of sparse DNNs using our proposed class of sparse neural networks called HBsNN (Hierarchical Block sparse Neural Networks).
Efficient Inferencing of Compressed Deep Neural Networks
Large number of weights in deep neural networks makes the models difficult to be deployed in low memory environments such as, mobile phones, IOT edge devices as well as "inferencing as a service" environments on cloud.
