# XNOR Neural Engine: A Hardware Accelerator IP for 21.6-fJ/op Binary Neural Network Inference

@article{Conti2018XNORNE, title={XNOR Neural Engine: A Hardware Accelerator IP for 21.6-fJ/op Binary Neural Network Inference}, author={Francesco Conti and Pasquale Davide Schiavone and Luca Benini}, journal={IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems}, year={2018}, volume={37}, pages={2940-2951} }

Binary neural networks (BNNs) are promising to deliver accuracy comparable to conventional deep neural networks at a fraction of the cost in terms of memory and energy. [] Key Result We show post-synthesis results in 65- and 22-nm technology for the XNE IP and post-layout results in 22 nm for the full MCU indicating that this system can drop the energy cost per binary operation to 21.6 fJ per operation at 0.4 V, and at the same time is flexible and performant enough to execute state-of-the-art BNN topologies…

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