Findings from published research, checked in the open

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1,212 claims from 763 papers are on the record. 44 have been checked so far; the other 1,168 have no check with a result yet.

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Keyword: VGG ×Clear all

6 claims from 3 papers

1. Computer Science › Advanced Neural Network Applications

   EIE

   Han, Liu, Mao et al. · ACM SIGARCH Computer Architecture News · 2016

   The authors propose EIE, a custom hardware engine that runs compressed neural networks directly, and report it is faster and far more energy efficient than CPU, GPU and DaDianNao comparisons.

   Unchecked2 claims
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   1. UncheckedThe EIE chip's energy savings come from four sources: moving weights from DRAM to SRAM (120×), sparsity (10×), weight sharing (8×) and skipping zero activations (3×).“Going from DRAM to SRAM gives EIE 120× energy saving; Exploiting sparsity saves 10×; Weight sharing gives 8×; Skipping zero activations from ReLU saves another 3×.”
   2. UncheckedThe paper reports that its EIE chip beats the DaDianNao accelerator by 2.9× in throughput, 19× in energy efficiency and 3× in area efficiency.“Compared with DaDianNao, EIE has 2.9×, 19× and 3× better throughput, energy efficiency and area efficiency.”

2. Computer Science › Advanced Neural Network Applications

   Picking Winning Tickets Before Training by Preserving Gradient Flow

   Wang, Zhang and Grosse · arXiv (Cornell University) · 2020

   Unchecked2 claims
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   1. Unchecked“Our method can prune 80% of the weights of a VGG-16 network on ImageNet at initialization, with only a 1.6% drop in top-1 accuracy.”
   2. Unchecked“Moreover, our method achieves significantly better performance than the baseline at extreme sparsity levels.”

3. Computer Science › Advanced Neural Network Applications

   GAT TransPruning: progressive channel pruning strategy combining graph attention network and transformer

   Lin, Wang and Lin · PeerJ Computer Science · 2024

   Unchecked2 claims
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   1. Unchecked“The experimental results reveal that the accuracy rate only drops by 6.58% when the channel pruning rate is 89% for VGG-19/CIFAR-100.”
   2. Unchecked“In addition, the lightweight model inference speed is 9.10 times faster than that of the original large model.”