Hardware-based real-time deep neural network lossless weights compression

Tomer Malach, Shlomo Greenberg, Moshe Haiut

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Deep Neural Networks (DNN) are widely applied to many mobile applications demanding real-time implementation and large memory space. Therefore, it presents a new challenge for low-power and efficient implementation of a diversity of applications, such as speech recognition and image classification, for embedded edge devices. This work presents a hardware-based DNN compression approach to address the limited memory resources in edge devices. We propose a new entropy-based compression algorithm for encoding DNN weights, as well as a real-time decoding method and efficient dedicated hardware implementation. The proposed approach enables a significant reduction of the required DNN weights memory (approximately 70% and 63% for AlexNet and VGG19, respectively), while allowing the decoding of one weight per clock cycle. Results show a high compression ratio compared to well-known lossless compression algorithms. The proposed hardware decoder enables an efficient implementation of large DNN networks in low-power edge devices with limited memory resources.

Original languageEnglish
Pages (from-to)205051-205060
Number of pages10
JournalIEEE Access
StatePublished - 1 Jan 2020


  • Deep neural network
  • Entropy compression
  • Hardware decoder
  • Real-time

ASJC Scopus subject areas

  • Computer Science (all)
  • Materials Science (all)
  • Engineering (all)


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