Abstract
In this work, we review Binarized Neural Networks (BNNs). BNNs are deep neural networks that use binary values for activations and weights, instead of full precision values. With binary values, BNNs can execute computations using bitwise operations, which reduces execution time. Model sizes of BNNs are much smaller than their full precision counterparts. While the accuracy of a BNN model is generally less than full precision models, BNNs have been closing accuracy gap and are becoming more accurate on larger datasets like ImageNet. BNNs are also good candidates for deep learning implementations on FPGAs and ASICs due to their bitwise efficiency. We give a tutorial of the general BNN methodology and review various contributions, implementations and applications of BNNs.
Highlights
Deep neural networks (DNNs) are becoming more powerful
In this paper we focus networks based on the Binarized Neural Networks (BNNs) methodology first proposed by Courbariaux et al in [1] where both weights and activations only use binary values, and these binary values are used during both inference and backpropgation training
Since BNNs have received substantial attention from the digital design community, we focus on various implementation of BNNs on FPGAs
Summary
Deep neural networks (DNNs) are becoming more powerful. as DNN models become larger they require more storage and computational power. In this paper we focus networks based on the BNN methodology first proposed by Courbariaux et al in [1] where both weights and activations only use binary values, and these binary values are used during both inference and backpropgation training. From this original idea, various works have explored how to improve their accuracy and how to implement them in low power and resource constrained platforms. In this paper we explain the basics of BNNs and review recent developments in this growing area Most work in this area has focused on advantages that are gained during inference time. FPGA and ASIC implementations are highlighted in Sections 8.1 and 8.5
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
