Energy-Efficient Convolutional Neural Networks via Recurrent Data Reuse

2019 ◽  
Author(s):  
Luca Mocerino ◽  
Valerio Tenace ◽  
Andrea Calimera
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Energy Efficient ◽  
Data Reuse

AxR-NN: Approximate Computation Reuse for Energy-Efficient Convolutional Neural Networks

2020 ◽  
Author(s):  
Dongning Ma ◽  
Xunzhao Yin ◽  
Michael Niemier ◽  
X. Sharon Hu ◽  
Xun Jiao
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Energy Efficient ◽  
Approximate Computation

RiSA: A Reinforced Systolic Array for Depthwise Convolutions and Embedded Tensor Reshaping

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-20
Author(s):  
Hyungmin Cho
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Systolic Array ◽  
Data Reuse ◽  
Systolic Arrays ◽  
High Data ◽  
Area Efficiency ◽  
High Area ◽  
Accelerator Design

Depthwise convolutions are widely used in convolutional neural networks (CNNs) targeting mobile and embedded systems. Depthwise convolution layers reduce the computation loads and the number of parameters compared to the conventional convolution layers. Many deep neural network (DNN) accelerators adopt an architecture that exploits the high data-reuse factor of DNN computations, such as a systolic array. However, depthwise convolutions have low data-reuse factor and under-utilize the processing elements (PEs) in systolic arrays. In this paper, we present a DNN accelerator design called RiSA, which provides a novel mechanism that boosts the PE utilization for depthwise convolutions on a systolic array with minimal overheads. In addition, the PEs in systolic arrays can be efficiently used only if the data items ( tensors ) are arranged in the desired layout. Typical DNN accelerators provide various types of PE interconnects or additional modules to flexibly rearrange the data items and manage data movements during DNN computations. RiSA provides a lightweight set of tensor management tasks within the PE array itself that eliminates the need for an additional module for tensor reshaping tasks. Using this embedded tensor reshaping, RiSA supports various DNN models, including convolutional neural networks and natural language processing models while maintaining a high area efficiency. Compared to Eyeriss v2, RiSA improves the area and energy efficiency for MobileNet-V1 inference by 1.91× and 1.31×, respectively.

Energy-Efficient Convolutional Neural Networks with Deterministic Bit-Stream Processing

2019 ◽  
Author(s):  
S. Rasoul Faraji ◽  
M. Hassan Najafi ◽  
Bingzhe Li ◽  
David J. Lilja ◽  
Kia Bazargan
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Energy Efficient ◽  
Stream Processing ◽  
Bit Stream

scholarly journals Energy Efficient Convolutional Neural Networks for EEG Artifact Detection

2018 ◽  
Author(s):  
Mohit Khatwani ◽  
M. Hosseini ◽  
H. Paneliya ◽  
Tinoosh Mohsenin ◽  
W. David Hairston ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Energy Efficient ◽  
Artifact Detection
Sign in / Sign up

Export Citation Format

Share Document