Normalization and dropout for stochastic computing-based deep convolutional neural networks

Ji Li, Zihao Yuan, Zhe Li, Ao Ren, Caiwen Ding, Jeffrey Draper, Shahin Nazarian, Qinru Qiu, Bo Yuan, Yanzhi Wang

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Recently, Deep Convolutional Neural Network (DCNN) has been recognized as the most effective model for pattern recognition and classification tasks. With the fast growing Internet of Things (IoTs) and wearable devices, it becomes attractive to implement DCNNs in embedded and portable systems. However, novel computing paradigms are urgently required to deploy DCNNs that have huge power consumptions and complex topologies in systems with limited area and power supply. Recent works have demonstrated that Stochastic Computing (SC) can radically simplify the hardware implementation of arithmetic units and has the potential to bring the success of DCNNs to embedded systems. This paper introduces normalization and dropout, which are essential techniques for the state-of-the-art DCNNs, to the existing SC-based DCNN frameworks. In this work, the feature extraction block of DCNNs is implemented using an approximate parallel counter, a near-max pooling block and an SC-based rectified linear activation unit. A novel SC-based normalization design is proposed, which includes a square and summation unit, an activation unit and a division unit. The dropout technique is integrated into the training phase and the learned weights are adjusted during the hardware implementation. Experimental results on AlexNet with the ImageNet dataset show that the SC-based DCNN with the proposed normalization and dropout techniques achieves 3.26% top-1 accuracy improvement and 3.05% top-5 accuracy improvement compared with the SC-based DCNN without these two essential techniques, confirming the effectiveness of our normalization and dropout designs.

Original languageEnglish (US)
Pages (from-to)395-403
Number of pages9
StatePublished - Mar 2019


  • Deep convolutional neural networks
  • Deep learning
  • Dropout
  • Normalization

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Electrical and Electronic Engineering


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