Mar 25, 2021 · In this article, we survey approaches to the problem of quantizing the numerical values in deep Neural Network computations, covering the ...
In this article, we survey approaches to the problem of quantizing the numerical values in deep Neural Network computations, covering the advantages/ ...
This chapter provides approaches to the problem of quantizing the numerical values in deep Neural Network computations, covering the advantages/disadvantages of ...
Mar 25, 2021 · This article surveys approaches to the problem of quantizing the numerical values in deep Neural Network computations, covering the ...
Quantization involves representing the input values from a continuous range as discrete values. Another study utilized this approach to construct a model by ...
Summary (Simulated vs Integer-only Quantization). In general integer-only and dyadic quantization are more desirable as compared to simulated/fake quantization.
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This document surveys quantization methods for efficient neural network inference. Quantization involves representing continuous real-valued numbers with a ...
This paper surveys the many neural network quantization techniques that have been developed in the last decade and proposes future directions of research in ...
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What is quantization in a neural network?
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Apr 7, 2021 · Bibliographic details on A Survey of Quantization Methods for Efficient Neural Network Inference.
We present a comprehensive survey of quantization concepts and methods, with a focus on image classification.
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