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Taylor Barton, Hao Yu, Kyle Rogers, Nancy Fulda, Shiuh-hua Wood Chiang, Jordan Yorgason and Karl F. Warnick
We present a transfer learning method inspired by modulatory neurotransmitter mechanisms in biological brains and explore applications for neuromorphic hardware. In this method, the pre-trained weights of an artificial neural network are held constant an...
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Sepehr Tabrizchi, Shaahin Angizi and Arman Roohi
Convolutional Neural Networks (CNNs), due to their recent successes, have gained lots of attention in various vision-based applications. They have proven to produce incredible results, especially on big data, that require high processing demands. However...
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Daniel Reiser, Peter Reichel, Stefan Pechmann, Maen Mallah, Maximilian Oppelt, Amelie Hagelauer, Marco Breiling, Dietmar Fey and Marc Reichenbach
In embedded applications that use neural networks (NNs) for classification tasks, it is important to not only minimize the power consumption of the NN calculation, but of the whole system. Optimization approaches for individual parts exist, such as quant...
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Tommaso Zanotti, Francesco Maria Puglisi and Paolo Pavan
Different in-memory computing paradigms enabled by emerging non-volatile memory technologies are promising solutions for the development of ultra-low-power hardware for edge computing. Among these, SIMPLY, a smart logic-in-memory architecture, provides h...
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Marco A. Martínez Ramírez, Emmanouil Benetos and Joshua D. Reiss
Virtual analog modeling of audio effects consists of emulating the sound of an audio processor reference device. This digital simulation is normally done by designing mathematical models of these systems. It is often difficult because it seeks to accurat...
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