3 citations · 4 across the 4 of their papers we have counts for
6 papers
Block Walsh-Hadamard Transform Based Binary Layers in Deep Neural Networks
Hongyi Pan, Diaa Badawi, Ahmet Enis Cetin
Convolution has been the core operation of modern deep neural networks. It is well-known that convolutions can be implemented in the Fourier Transform domain. In this paper, we pro…
Robust Principal Component Analysis Using a Novel Kernel Related with the L1-Norm
Hongyi Pan, Diaa Badawi, Erdem Koyuncu +1
We consider a family of vector dot products that can be implemented using sign changes and addition operations only. The dot products are energy-efficient as they avoid the multipl…
Fast Walsh-Hadamard Transform and Smooth-Thresholding Based Binary Layers in Deep Neural Networks
Hongyi Pan, Diaa Dabawi, Ahmet Enis Cetin
In this paper, we propose a novel layer based on fast Walsh-Hadamard transform (WHT) and smooth-thresholding to replace convolution layers in deep neural networks. In t…
MF-Net: Compute-In-Memory SRAM for Multibit Precision Inference using Memory-immersed Data Conversion and Multiplication-free Operators
Shamma Nasrin, Diaa Badawi, Ahmet Enis Cetin +2
We propose a co-design approach for compute-in-memory inference for deep neural networks (DNN). We use multiplication-free function approximators based on ell_1 norm along with a c…
Discrete Cosine Transform Based Causal Convolutional Neural Network for Drift Compensation in Chemical Sensors
Diaa Badawi, Agamyrat Agambayev, Sule Ozev +1
Sensor drift is a major problem in chemical sensors that requires addressing for reliable and accurate detection of chemical analytes. In this paper, we develop a causal convolutio…
Detecting Gas Vapor Leaks Using Uncalibrated Sensors
Diaa Badawi, Tuba Ayhan, Sule Ozev +3
Chemical and infra-red sensors generate distinct responses under similar conditions because of sensor drift, noise or resolution errors. In this work, we use different time-series…