no code implementations • 5 Nov 2018 • Ahmed T. Elthakeb, Prannoy Pilligundla, FatemehSadat Mireshghallah, Amir Yazdanbakhsh, Hadi Esmaeilzadeh
We show how ReLeQ can balance speed and quality, and provide an asymmetric general solution for quantization of a large variety of deep networks (AlexNet, CIFAR-10, LeNet, MobileNet-V1, ResNet-20, SVHN, and VGG-11) that virtually preserves the accuracy (=< 0. 3% loss) while minimizing the computation and storage cost.
no code implementations • 4 May 2019 • Ahmed T. Elthakeb, Prannoy Pilligundla, Hadi Esmaeilzadeh
To further mitigate this loss, we propose a novel sinusoidal regularization, called SinReQ1, for deep quantized training.
1 code implementation • 30 May 2019 • Byung Hoon Ahn, Prannoy Pilligundla, Hadi Esmaeilzadeh
Further experiments also confirm that our adaptive sampling can even improve AutoTVM's simulated annealing by 4. 00x.
no code implementations • ICML 2020 • Ahmed T. Elthakeb, Prannoy Pilligundla, Alex Cloninger, Hadi Esmaeilzadeh
The deep layers of modern neural networks extract a rather rich set of features as an input propagates through the network.
1 code implementation • ICLR 2020 • Byung Hoon Ahn, Prannoy Pilligundla, Amir Yazdanbakhsh, Hadi Esmaeilzadeh
This solution dubbed Chameleon leverages reinforcement learning whose solution takes fewer steps to converge, and develops an adaptive sampling algorithm that not only focuses on the costly samples (real hardware measurements) on representative points but also uses a domain-knowledge inspired logic to improve the samples itself.
no code implementations • 29 Feb 2020 • Ahmed T. Elthakeb, Prannoy Pilligundla, FatemehSadat Mireshghallah, Tarek Elgindi, Charles-Alban Deledalle, Hadi Esmaeilzadeh
We show how SINAREQ balance compute efficiency and accuracy, and provide a heterogeneous bitwidth assignment for quantization of a large variety of deep networks (AlexNet, CIFAR-10, MobileNet, ResNet-18, ResNet-20, SVHN, and VGG-11) that virtually preserves the accuracy.
1 code implementation • 1 Jan 2021 • Ahmed T. Elthakeb, Prannoy Pilligundla, Tarek Elgindi, FatemehSadat Mireshghallah, Charles-Alban Deledalle, Hadi Esmaeilzadeh
We show how WaveQ balance compute efficiency and accuracy, and provide a heterogeneous bitwidth assignment for quantization of a large variety of deep networks (AlexNet, CIFAR-10, MobileNet, ResNet-18, ResNet-20, SVHN, and VGG-11) that virtually preserves the accuracy.