QONNX: Representing Arbitrary-Precision Quantized Neural Networks

We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks. We first introduce support for low precision quantization in existing ONNX-based quantization formats by leveraging integer clipping, result...

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Detalles Bibliográficos
Autores principales: Pappalardo, Alessandro, Umuroglu, Yaman, Blott, Michaela, Mitrevski, Jovan, Hawks, Ben, Tran, Nhan, Loncar, Vladimir, Summers, Sioni, Borras, Hendrik, Muhizi, Jules, Trahms, Matthew, Hsu, Shih-Chieh, Hauck, Scott, Duarte, Javier
Lenguaje:eng
Publicado: 2022
Materias:
stat.ML
Mathematical Physics and Mathematics
cs.PL
Computing and Computers
cs.AR
cs.LG
Acceso en línea:http://cds.cern.ch/record/2813346

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