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04/14/2023
AUTOSPARSE: Towards Automated Sparse Training of Deep Neural Networks
Sparse training is emerging as a promising avenue for reducing the compu...
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04/12/2021
Tensor Processing Primitives: A Programming Abstraction for Efficiency and Portability in Deep Learning Workloads
During the past decade, novel Deep Learning (DL) algorithms/workloads an...
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09/17/2019
K-TanH: Hardware Efficient Activations For Deep Learning
We propose K-TanH, a novel, highly accurate, hardware efficient approxim...
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05/29/2019
A Study of BFLOAT16 for Deep Learning Training
This paper presents the first comprehensive empirical study demonstratin...
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07/15/2017
Ternary Residual Networks
Sub-8-bit representation of DNNs incur some discernible loss of accuracy...
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05/02/2017
Ternary Neural Networks with Fine-Grained Quantization
We propose a novel fine-grained quantization (FGQ) method to ternarize p...
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01/31/2017
Mixed Low-precision Deep Learning Inference using Dynamic Fixed Point
We propose a cluster-based quantization method to convert pre-trained fu...
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08/13/2015
A Randomized Rounding Algorithm for Sparse PCA
We present and analyze a simple, two-step algorithm to approximate the o...
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03/22/2015
Relaxed Leverage Sampling for Low-rank Matrix Completion
We consider the problem of exact recovery of any m× n matrix of rank ϱ f...
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03/12/2015
Approximating Sparse PCA from Incomplete Data
We study how well one can recover sparse principal components of a data ...
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03/02/2015