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06/18/2023
DropCompute: simple and more robust distributed synchronous training via compute variance reduction
Background: Distributed training is essential for large scale training o...
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03/21/2022
Optimal Fine-Grained N:M sparsity for Activations and Neural Gradients
In deep learning, fine-grained N:M sparsity reduces the data footprint a...
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02/06/2022
Energy awareness in low precision neural networks
Power consumption is a major obstacle in the deployment of deep neural n...
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01/30/2022
On Recoverability of Graph Neural Network Representations
Despite their growing popularity, graph neural networks (GNNs) still hav...
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12/19/2021
Logarithmic Unbiased Quantization: Practical 4-bit Training in Deep Learning
Quantization of the weights and activations is one of the main methods t...
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02/16/2021
Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks
Recently, researchers proposed pruning deep neural network weights (DNNs...
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12/09/2020
GAN Steerability without optimization
Recent research has shown remarkable success in revealing "steering" dir...
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06/15/2020
Neural gradients are lognormally distributed: understanding sparse and quantized training
Neural gradient compression remains a main bottleneck in improving train...
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02/18/2020
Robust Quantization: One Model to Rule Them All
Neural network quantization methods often involve simulating the quantiz...
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11/17/2019
Loss Aware Post-training Quantization
Neural network quantization enables the deployment of large models on re...
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09/25/2019
CAT: Compression-Aware Training for bandwidth reduction
Convolutional neural networks (CNNs) have become the dominant neural net...
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09/17/2019
Thanks for Nothing: Predicting Zero-Valued Activations with Lightweight Convolutional Neural Networks
Convolutional neural networks (CNNs) introduce state-of-the-art results ...
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05/26/2019
Feature Map Transform Coding for Energy-Efficient CNN Inference
Convolutional neural networks (CNNs) achieve state-of-the-art accuracy i...
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10/02/2018
ACIQ: Analytical Clipping for Integer Quantization of neural networks
Unlike traditional approaches that focus on the quantization at the netw...
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05/25/2018
Scalable Methods for 8-bit Training of Neural Networks
Quantized Neural Networks (QNNs) are often used to improve network effic...
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03/05/2018