Energy Efficient CNN Inference Accelerator using Fast Fourier Transform
碩士 === 國立交通大學 === 電子研究所 === 107 === In recent years, Deep Convolutional Neural Networks (DCNNs) are state-of-the-art for various classification tasks, but are computationally expensive due to the high-dimensional convolutions. In the thesis, we proposed using FFT-based convolution in frequency domai...
Main Authors: | Chung, Ya-Chin, 鍾亞晉 |
---|---|
Other Authors: | Liu, Chih-Wei |
Format: | Others |
Language: | en_US |
Published: |
2018
|
Online Access: | http://ndltd.ncl.edu.tw/handle/275wrz |
Similar Items
-
Optimization of Data Locality for Energy-Efficient Pointwise Convolutional Layers in CNN Inference Accelerators
by: Wu, Hao-Ning, et al.
Published: (2018) -
In Search of the Performance- And Energy-Efficient CNN Accelerators
by: Sedukhin, S., et al.
Published: (2022) -
A fast Fourier Transform accelerator for a transputer system
by: Dodge, Christopher J.
Published: (1993) -
Soft Error Analysis for CNN Inference Accelerator with Efficient Dynamic Fixed Point Quantization
by: Wu, Yueh-Chi, et al.
Published: (2018) -
A CNN-based Classfication Method Using Principle Component Analysis and Fast Fourier Transform for Complex Images
by: HSIANG,CHIEN-HUA, et al.
Published: (2019)