Accumulation-Aware Shift and Difference-Add Booth Multiplier for Energy-Efficient Convolutional Neural Network Inference

2021 ◽  
Author(s):  
Zhong-Da Wu ◽  
Shanq-Jang Ruan ◽  
Bai-Kui Yan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Energy Efficient ◽  
Network Inference ◽  
Booth Multiplier

scholarly journals Methods for Preventing Visual Attacks in Convolutional Neural Networks Based on Data Discard and Dimensionality Reduction

2021 ◽  
Vol 11 (11) ◽  
pp. 5235
Author(s):  
Nikita Andriyanov
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Inference ◽  
Recognition Accuracy ◽  
Network Architectures ◽  
Reduced Dimensions ◽  
Different Types ◽  
Rectangular Area ◽  
Noise Impulse ◽  
Selection Of

The article is devoted to the study of convolutional neural network inference in the task of image processing under the influence of visual attacks. Attacks of four different types were considered: simple, involving the addition of white Gaussian noise, impulse action on one pixel of an image, and attacks that change brightness values within a rectangular area. MNIST and Kaggle dogs vs. cats datasets were chosen. Recognition characteristics were obtained for the accuracy, depending on the number of images subjected to attacks and the types of attacks used in the training. The study was based on well-known convolutional neural network architectures used in pattern recognition tasks, such as VGG-16 and Inception_v3. The dependencies of the recognition accuracy on the parameters of visual attacks were obtained. Original methods were proposed to prevent visual attacks. Such methods are based on the selection of “incomprehensible” classes for the recognizer, and their subsequent correction based on neural network inference with reduced image sizes. As a result of applying these methods, gains in the accuracy metric by a factor of 1.3 were obtained after iteration by discarding incomprehensible images, and reducing the amount of uncertainty by 4–5% after iteration by applying the integration of the results of image analyses in reduced dimensions.

Rank order coding based spiking convolutional neural network architecture with energy-efficient membrane voltage updates

2020 ◽  
Vol 407 ◽  
pp. 300-312 ◽  
Author(s):  
Hoyoung Tang ◽  
Donghyeon Cho ◽  
Dongwoo Lew ◽  
Taehwan Kim ◽  
Jongsun Park
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Energy Efficient ◽  
Network Architecture ◽  
Rank Order ◽  
Membrane Voltage ◽  
Neural Network Architecture

An Accurate, Error-Tolerant, and Energy-Efficient Neural Network Inference Engine Based on SONOS Analog Memory

2022 ◽  
pp. 1-14
Author(s):  
T. Patrick Xiao ◽  
Ben Feinberg ◽  
Christopher H. Bennett ◽  
Vineet Agrawal ◽  
Prashant Saxena ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Efficient ◽  
Network Inference ◽  
Inference Engine ◽  
Analog Memory
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