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Jun 20, 2024 · In this article, we present a new method for implementing a neural network whose weights are floating-point numbers on a fixed-point architecture.
In this article, we present a new method for implementing a neural network whose weights are floating-point numbers on a fixed-point architecture.
Our quantization is based on mixed-integer linear programming (MILP) and leverages the unique structure of neural networks and effective over-approximations to ...
Dorra Ben Khalifa , Matthieu Martel : Efficient Implementation of Neural Networks Usual Layers on Fixed-Point Architectures. LCTES 2024: 12-22.
Efficient Implementation of Neural Networks Usual Layers on Fixed-Point Architectures · Fonction : Auteur · PersonId : 843157 · ORCID : 0000-0002-6238-9651.
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We present scalable and generalized fixed-point hardware designs (source VHDL code is provided) for Artificial Neural Networks (ANNs).
Feb 4, 2022 · The experimental results obtained show the efficiency of our method which can ensure that the new fixed-point neural network has the same.
We use fixed point theory to analyze nonnegative neural networks, which we define as neural networks that map nonnegative vectors to nonnegative vectors.
This chapter will introduce the intuition behind these DEQ models, discuss some of the theoretial aspects of the approaches, and then present a medium-scale ...
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Apr 1, 2022 · Abstract—A deep neural network based equalizer is proposed to mitigate the intersymbol interference observed in next generation.