Search Results for author:
Found 6 papers, 0 papers with code
S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration
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
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
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
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
Kronecker Products (KP) have been used to compress IoT RNN Applications by 15-38x compression factors, achieving better results than traditional compression methods.
Learning low-precision neural networks without Straight-Through Estimator(STE)
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.
