NEURAL NETWORK INFERENCE QUANTIZATION

NEURAL NETWORK INFERENCE QUANTIZATION

One or more computer processors responsive to neural network run-time, reduce one or more sets of maximum activations along a hidden dimension respectively associated with one or more activation tensors and one or more layers of a neural network. The one or more computer processors compute an interq...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Quinn, Jerome L, Ward, Robert Todd, EL-KURDI, YOUSEF
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
ELECTRIC DIGITAL DATA PROCESSING
PHYSICS
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Beschreibung
Zusammenfassung:One or more computer processors responsive to neural network run-time, reduce one or more sets of maximum activations along a hidden dimension respectively associated with one or more activation tensors and one or more layers of a neural network. The one or more computer processors compute an interquartile range (IQR) clip threshold for each reduced set for each sequence dimension in the neural network. The one or more computer processors clip one or more activations based on respective computed IQR clip thresholds. The one or more computer processors quantize the clipped activations.