Deep neural network on field-programmable gate array

A field programmable gate array (FPGA) may be used for inference of a trained deep neural network (DNN). The trained DNN may comprise a set of parameters and the FPGA may have a first precision configuration defining first number representations of the set of parameters. The FPGA may determine diffe...

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Bibliographische Detailangaben
Hauptverfasser:	Diamantopoulos, Dionysios, Polig, Raphael, Purandare, Mitra
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS
Online-Zugang:	Volltext bestellen
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Beschreibung
Zusammenfassung:	A field programmable gate array (FPGA) may be used for inference of a trained deep neural network (DNN). The trained DNN may comprise a set of parameters and the FPGA may have a first precision configuration defining first number representations of the set of parameters. The FPGA may determine different precision configurations of the trained DNN. A precision configuration of the precision configurations may define second number representations of a subset of the set of parameters. For each precision configuration of the determined precision configurations a bitstream file may be provided. The bitstream files may be stored so that the FPGA may be programmed using one of the stored bitstream files for inference of the trained DNN.