@inbook{9e4172c8e15e41c384bf3a50dad88997,
title = "Acceleration of Convolutional Networks Using Nanoscale Memristive Devices",
abstract = "We discuss a convolutional neural network for handwritten digit classification and its hardware acceleration as an inference engine using nanoscale memristive devices in the spike domain. We study the impact of device programming variability on the spiking neural network{\textquoteright}s (SNN) inference accuracy and benchmark its performance with an equivalent artificial neural network (ANN). We demonstrate optimization strategies to implement these networks with memristive devices with an on-off ratio as low as 10 and only 32 levels of resolution. Further, close to baseline accuracies can be maintained for the networks even if such memristive devices are used to duplicate the pre-determined kernel weights to enable parallel execution of the convolution operation.",
keywords = "Artificial neural networks, Memristors, Non-volatile memory devices, Programming variability, Spiking neural networks",
author = "Kulkarni, {Shruti R.} and Babu, {Anakha V.} and Bipin Rajendran",
year = "2018",
month = jul,
day = "28",
doi = "10.1007/978-3-319-98204-5_20",
language = "English",
isbn = "9783319982038",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "240--251",
editor = "Elias Pimenidis and Chrisina Jayne",
booktitle = "Engineering Applications of Neural Networks - 19th International Conference, EANN 2018, Proceedings",
address = "Germany",
note = "19th International Conference on Engineering Applications of Neural Networks, EANN 2018 ; Conference date: 03-09-2018 Through 05-09-2018",
}