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Julian Faraone

2 accepted papers

2021

A Block Minifloat Representation for Training Deep Neural Networks

ICLR 2021poster

Training Deep Neural Networks (DNN) with high efficiency can be difficult to achieve with native floating-point representations and commercially available hardware. Specialized arithmetic with custom acceleration offers perhaps the most promising alternative. Ongoing research is trending towards nar…

Cited by 54SourcePDFScholar
2018

SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks

CVPR 2018poster

Inference for state-of-the-art deep neural networks is computationally expensive, making them difficult to deploy on constrained hardware environments. An efficient way to reduce this complexity is to quantize the weight parameters and/or activations during training by approximating their distributi…