Approximate Softmax Functions for Energy-Efficient Deep Neural Networks

Approximate computing has emerged as a new paradigm that provides power-efficient and high-performance arithmetic designs by relaxing the stringent requirement of accuracy. Nonlinear functions (such as softmax , rectified linear unit ( ReLU ), Tanh , and Sigmoid ) are extensively used in deep neural...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on very large scale integration (VLSI) systems Jg. 31; H. 1; S. 1 - 13
Hauptverfasser:	Chen, Ke, Gao, Yue, Waris, Haroon, Liu, Weiqiang, Lombardi, Fabrizio
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Accuracy Adders Approximate computing Artificial neural networks Circuit design Complexity Deep neural network (DNN) Dividers Energy dissipation field-programmable gate array (FPGA) implementation Hardware Hybrid structures Linear functions Logic gates Lookup tables Mathematical analysis Neural networks nonlinear function Power management softmax layer Table lookup Taylor series
ISSN:	1063-8210, 1557-9999
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!