Fully Integrated Analog Machine Learning Classifier Using Custom Activation Function for Low Resolution Image Classification

Author(s):  
Sanjeev Tannirkulam Chandrasekaran ◽  
Akshay Jayaraj ◽  
Vinay Elkoori Ghantala Karnam ◽  
Imon Banerjee ◽  
Arindam Sanyal
Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 515 ◽  
Author(s):  
Sanjeev T. Chandrasekaran ◽  
Ruobing Hua ◽  
Imon Banerjee ◽  
Arindam Sanyal

We propose a fully integrated common-source amplifier based analog artificial neural network (ANN). The performance of the proposed ANN with a custom non-linear activation function is demonstrated on the breast cancer classification task. A hardware-software co-design methodology is adopted to ensure good matching between the software AI model and hardware prototype. A 65 nm prototype of the proposed ANN is fabricated and characterized. The prototype ANN achieves 97% classification accuracy when operating from a 1.1 V supply with an energy consumption of 160 fJ/classification. The prototype consumes 50 μ W power and occupies 0.003 mm 2 die area.


2020 ◽  
Vol 47 (11) ◽  
pp. 1027-1031
Author(s):  
Sungho Shin ◽  
Joosoon Lee ◽  
Junseok Lee ◽  
Seungjun Choi ◽  
Kyoobin Lee

2020 ◽  
Vol 515 ◽  
pp. 233-247 ◽  
Author(s):  
Xiaobin Zhu ◽  
Zhuangzi Li ◽  
Xianbo Li ◽  
Shanshan Li ◽  
Feng Dai

Author(s):  
Sumit Kaur

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 


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