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Binary neural networks

Binary neural networks

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Convolutional neural networks (CNNs) are used in a spread spectrum of machine learning applications, such as computer vision and speech recognition. Computation and memory accesses are the major challenges for the deployment of CNNs in resource -limited and low -power embedded systems. The recently proposed binary neural networks (BNNs) use just 1 bit for weights and/or activations instead of full precision values, hence substitute complex multiply -accumulation operations with bitwise logic operations to reduce the computation and memory footprint drastically. However, most BNN models come with some accuracy loss, especially in big datasets. Improving the accuracy of BNNs and designing efficient hardware accelerator for them are two important research directions that have attracted many attentions in recent years. In this chapter, we conduct a survey on the state-of-the-art researches on the design and hardware implementation of the BNN models.

Chapter Contents:

  • 5.1 Introduction
  • 5.2 Binary neural networks
  • 5.2.1 Binary and ternary weights for neural networks
  • 5.2.2 Binarized and ternarized neural networks
  • 5.3 BNN optimization techniques
  • 5.4 Hardware implementation of BNNs
  • 5.5 Conclusion
  • References

Inspec keywords: convolutional neural nets; computer vision; Big Data; learning (artificial intelligence); speech recognition

Other keywords: hardware accelerator; full precision values; computer vision; convolutional neural networks; CNN; speech recognition; big datasets; spread spectrum; machine learning applications; binary neural networks; complex multiply-accumulation operations; BNN

Subjects: Neural computing techniques; Natural language processing; Speech processing techniques; Data handling techniques; Computer vision and image processing techniques

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