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arXiv · 1807.08716

NullaNet: Training Deep Neural Networks for Reduced-Memory-Access Inference

Mahdi Nazemi·Ghasem Pasandi·Massoud Pedram
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Abstract

Deep neural networks have been successfully deployed in a wide variety of applications including computer vision and speech recognition. However, computational and storage complexity of these models has forced the majority of computations to be performed on high-end computing platforms or on the cloud. To cope with computational and storage complexity of these models, this paper presents a training method that enables a radically different approach for realization of deep neural networks through Boolean logic minimization. The aforementioned realization completely removes the energy-hungry step of accessing memory for obtaining model parameters, consumes about two orders of magnitude fewer computing resources compared to realizations that use floatingpoint operations, and has a substantially lower latency.

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Mahdi Nazemi, Ghasem Pasandi, Massoud Pedram. 2018-07-23. NullaNet: Training Deep Neural Networks for Reduced-Memory-Access Inference. https://arxiv.org/abs/1807.08716

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