Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Binary neural networks

Binary neural networks

For access to this article, please select a purchase option:

Buy chapter PDF
$16.00
(plus tax if applicable)
Buy Knowledge Pack
10 chapters for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Hardware Architectures for Deep Learning — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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

Preview this chapter:
Zoom in
Zoomout

Binary neural networks, Page 1 of 2

| /docserver/preview/fulltext/books/cs/pbcs055e/PBCS055E_ch5-1.gif /docserver/preview/fulltext/books/cs/pbcs055e/PBCS055E_ch5-2.gif

Related content

content/books/10.1049/pbcs055e_ch5
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address