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16 May, 2022
Santa Clara Convention Center, California
Representing Fourier Transforms and Advanced Signal Processing as Convolutional Neural Networks
We present novel techniques for representing signal processing operations such as frequency-domain transforms, spectrogram generation, and MFCC extraction as neural networks. In many applications, signal processing operations are performed in a separate step before a neural network (i.e. data preprocessing) or as a layer inside the neural network itself. Examples of such applications include radar, audio, and medical imaging, all of which typically involve Fourier analysis. In such applications, it is often advantageous to represent signal processing operations in terms of common neural network layers, such as convolutions, elementwise and split/concatenate operations. By representing signal processing operations as neural networks, it is possible to execute them as part of a neural network.
About the speaker
Cagatay Dikici is a Senior Research Manager in the AI Research Team at Imagination Technologies in London. He received his PhD in Computer Science from INSA de Lyon, France. His research interests include machine learning, computer vision, and information theory in general. At Imagination, he works primarily on neural network accelerators and low-precision inference targeting embedded systems.
He has 8 years of experience as a researcher in the semiconductor IP industry, during which time he has led and contributed to more than 30 publications and patents.