Abstract
This paper considers the design of a binary scalar quantizer of Laplacian source and its application in compressed neural networks. The quantizer performance is investigated in a wide dynamic range of data variances, and for that purpose, we derive novel closed-form expressions. Moreover, we propose two selection criteria for the variance range of interest. Binary quantizers are further implemented for compressing neural network weights and its performance is analysed for a simple classification task. Good matching between theory and experiment is observed and a great possibility for implementation is indicated.
Highlights
Artificial neural networks (NNs) have become an attractive research field in recent decades for resolving different challenges due to the increasing availability of powerful hardware [1]
1Abstract—This paper considers the design of a binary scalar quantizer of Laplacian source and its application in compressed neural networks
The goal of the section is to verify the theoretical analysis provided in previous Section III by applying a binary quantizer in processing the weights of NN
Summary
Artificial neural networks (NNs) have become an attractive research field in recent decades for resolving different challenges due to the increasing availability of powerful hardware [1]. It is worth mentioning that the most significant achievements have been provided in tasks, such as image classification [2], object recognition [3], and speech processing [4]. The application in other fields has been performed, where some promising results have been achieved [5]–[7]. The improved performance (i.e., high prediction accuracy level) has often been provided using very complex NN architectures, with a large amount of parameters, computational and storage resources. This in turn can be a limiting factor for the application of NNs in portable and edge computing devices with limited memory and processing power, or in latency-critical services. NN parameters (weights, activations, etc.), usually represented in 32-bits floating point format (full precision), are mapped to fixed-point representations using lower bit lengths
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