GANPU: A 135TFLOPS/W Multi-DNN Training Processor for GANs with Speculative Dual-Sparsity Exploitation

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Generative adversarial networks (GAN) have a wide range of applications, from image style transfer to synthetic voice generation [1]. GAN applications on mobile devices, such as face-to-Emoji conversion and super-resolution imaging, enable more engaging user interaction. As shown in Fig. 7.4.1, a GAN consists of 2 competing deep neural networks (DNN): a generator and a discriminator. The discriminator is trained, while the generator is fixed, to distinguish whether the generated image is real or fake. On the other hand, the generator is trained to generate fake images to fool the discriminator. The minimax rivalry between the 2 sub-DNNs enables the model to generate high-quality images, difficult even for humans to distinguish.
Publisher
Institute of Electrical and Electronics Engineers Inc.
Issue Date
2020-02-17
Language
English
Citation

IEEE International Solid-State Circuits Conference, ISSCC 2020, pp.140 - 142

ISSN
0193-6530
DOI
10.1109/ISSCC19947.2020.9062989
URI
http://hdl.handle.net/10203/278509
Appears in Collection
EE-Conference Papers(학술회의논문)
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