Search Results for author: Zhi-Gang Liu

Found 6 papers, 0 papers with code

S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration

no code implementations16 Jul 2021 Zhi-Gang Liu, Paul N. Whatmough, Yuhao Zhu, Matthew Mattina

We propose to exploit structured sparsity, more specifically, Density Bound Block (DBB) sparsity for both weights and activations.

Sparse Systolic Tensor Array for Efficient CNN Hardware Acceleration

no code implementations4 Sep 2020 Zhi-Gang Liu, Paul N. Whatmough, Matthew Mattina

In this paper, we address a key architectural challenge with structural sparsity: how to provide support for a range of sparsity levels while maintaining high utilization of the hardware.

Efficient Residue Number System Based Winograd Convolution

no code implementations ECCV 2020 Zhi-Gang Liu, Matthew Mattina

Prior research has shown that Winograd algorithm can reduce the computational complexity of convolutional neural networks (CNN) with weights and activations represented in floating point.

Systolic Tensor Array: An Efficient Structured-Sparse GEMM Accelerator for Mobile CNN Inference

no code implementations16 May 2020 Zhi-Gang Liu, Paul N. Whatmough, Matthew Mattina

Convolutional neural network (CNN) inference on mobile devices demands efficient hardware acceleration of low-precision (INT8) general matrix multiplication (GEMM).

Compressing Language Models using Doped Kronecker Products

no code implementations24 Jan 2020 Urmish Thakker, Paul N. Whatmough, Zhi-Gang Liu, Matthew Mattina, Jesse Beu

Kronecker Products (KP) have been used to compress IoT RNN Applications by 15-38x compression factors, achieving better results than traditional compression methods.

Language Modelling

Learning low-precision neural networks without Straight-Through Estimator(STE)

no code implementations4 Mar 2019 Zhi-Gang Liu, Matthew Mattina

The Straight-Through Estimator (STE) is widely used for back-propagating gradients through the quantization function, but the STE technique lacks a complete theoretical understanding.


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