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![]() Title:Neural Network Precision Tuning Using Stochastic Arithmetic Conference:NSV 2022 Tags:Auto-Tuning, Floating-Point, Neural Networks, Precision and Stochastic Arithmetic Abstract: Neural networks can be costly in terms of memory and execution time. Reducing their cost has become an objective, especially when integrated in an embedded system with limited resources. A possible solution consists in reducing the precision of their neurons parameters. In this article, we present how to use auto-tuning on neural networks to lower their precision while keeping an accurate output. To do so, we use a floating-point auto-tuning tool on different kinds of neural networks. We show that, to some extent, we can lower the precision of several neural network parameters without compromising the accuracy requirement. Neural Network Precision Tuning Using Stochastic Arithmetic ![]() Neural Network Precision Tuning Using Stochastic Arithmetic | ||||
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