TY - GEN
T1 - On the energy benefits of spiking deep neural networks
T2 - 2016 International Joint Conference on Neural Networks, IJCNN 2016
AU - Han, Bing
AU - Sengupta, Abhronil
AU - Roy, Kaushik
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/31
Y1 - 2016/10/31
N2 - Deep learning neural networks have achieved success in a large number of visual processing tasks and are currently utilized for many real-world applications like image search and speech recognition among others. However, in spite of achieving high accuracy in such classification problems, they involve significant computational resources. Over the past few years, artificial neural network models have evolved into the biologically realistic and event-driven spiking neural networks. Recent research efforts have been directed at developing mechanisms to convert traditional deep artificial nets to spiking nets where the neurons communicate by means of spikes. However, there have been limited studies providing insights on the specific power, area and energy benefits offered by deep spiking neural nets in comparison to their non-spiking counterparts. In this paper, we perform a case study for a hardware implementation of a spiking/non-spiking deep net on the MNIST dataset and clearly outline the design prospects involved in implementing neural computing platforms in the spiking mode of operation.
AB - Deep learning neural networks have achieved success in a large number of visual processing tasks and are currently utilized for many real-world applications like image search and speech recognition among others. However, in spite of achieving high accuracy in such classification problems, they involve significant computational resources. Over the past few years, artificial neural network models have evolved into the biologically realistic and event-driven spiking neural networks. Recent research efforts have been directed at developing mechanisms to convert traditional deep artificial nets to spiking nets where the neurons communicate by means of spikes. However, there have been limited studies providing insights on the specific power, area and energy benefits offered by deep spiking neural nets in comparison to their non-spiking counterparts. In this paper, we perform a case study for a hardware implementation of a spiking/non-spiking deep net on the MNIST dataset and clearly outline the design prospects involved in implementing neural computing platforms in the spiking mode of operation.
UR - http://www.scopus.com/inward/record.url?scp=85007277316&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85007277316&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2016.7727303
DO - 10.1109/IJCNN.2016.7727303
M3 - Conference contribution
AN - SCOPUS:85007277316
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 971
EP - 976
BT - 2016 International Joint Conference on Neural Networks, IJCNN 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 July 2016 through 29 July 2016
ER -