TY - GEN
T1 - LATTE
T2 - 2016 International Joint Conference on Neural Networks, IJCNN 2016
AU - Tsai, Wei Yu
AU - Barch, Davis R.
AU - Cassidy, Andrew S.
AU - DeBole, Michael V.
AU - Andreopoulos, Alexander
AU - Jackson, Bryan L.
AU - Flickner, Myron D.
AU - Modha, Dharmendra S.
AU - Sampson, Jack
AU - Narayanan, Vijaykrishnan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/31
Y1 - 2016/10/31
N2 - With recent advances in silicon technology, previously intractable Deep Neural Network (DNN) solutions to complex visual, auditory, and other sensory perception problems are now practical for real-time, energy constrained systems. One such advancement is IBM's TrueNorth neurosynaptic processor, containing 1 million neurons and 256 million synapses, consuming 65mW of power, and capable of operating in real-time for a variety of applications. In this work, we explore how auditory features can be extracted on the TrueNorth processor using low numerical precision while maintaining algorithmic fidelity for DNN based spoken digit recognition on isolated words from the TIDIGITS dataset. Further, we show that our Low-power Audio Transform with TrueNorth Ecosystem (LATTE) is capable of achieving a 24× reduction in energy for feature extraction over a baseline FPGA implementation using standard MFCC audio features, while only incurring a 3-6% accuracy penalty.
AB - With recent advances in silicon technology, previously intractable Deep Neural Network (DNN) solutions to complex visual, auditory, and other sensory perception problems are now practical for real-time, energy constrained systems. One such advancement is IBM's TrueNorth neurosynaptic processor, containing 1 million neurons and 256 million synapses, consuming 65mW of power, and capable of operating in real-time for a variety of applications. In this work, we explore how auditory features can be extracted on the TrueNorth processor using low numerical precision while maintaining algorithmic fidelity for DNN based spoken digit recognition on isolated words from the TIDIGITS dataset. Further, we show that our Low-power Audio Transform with TrueNorth Ecosystem (LATTE) is capable of achieving a 24× reduction in energy for feature extraction over a baseline FPGA implementation using standard MFCC audio features, while only incurring a 3-6% accuracy penalty.
UR - http://www.scopus.com/inward/record.url?scp=85007155350&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85007155350&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2016.7727757
DO - 10.1109/IJCNN.2016.7727757
M3 - Conference contribution
AN - SCOPUS:85007155350
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 4270
EP - 4277
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 -