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Deep neural networks are typically represented by a much larger number of parameters than shallow models, making them prohibitive for small footprint devices. Recent research shows that there is considerable redundancy in the parameter space of deep neural networks. In this paper, we propose a method to compress deep neural networks by using the Fisher Information metric, which we estimate through...
Approximate/inexact computing has become an attractive approach for designing high performance and low power arithmetic circuits. Floating-point (FP) arithmetic is required in many applications, such as digital signal processing and machine learning. Different approximate FP multipliers are proposed in this paper, the accuracy and the circuit requirements of these designs are assessed to select the...
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