Verification of Binarized Neural Networks via Inter-neuron Factoring

(Short Paper)
  • Chih-Hong ChengEmail author
  • Georg Nührenberg
  • Chung-Hao Huang
  • Harald Ruess
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11294)


Binarized Neural Networks (BNN) have recently been proposed as an energy-efficient alternative to more traditional learning networks. Here we study the problem of formally verifying BNNs by reducing it to a corresponding hardware verification problem. The main step in this reduction is based on factoring computations among neurons within a hidden layer of the BNN in order to make the BNN verification problem more scalable in practice. The main contributions of this paper include results on the NP-hardness and hardness of PTAS approximability of this essential optimization and factoring step, and we design polynomial-time search heuristics for generating approximate factoring solutions. With these techniques we are able to scale the verification problem to moderately-sized BNNs for embedded devices with thousands of neurons and inputs.



We thank Dr. Ljubo Mercep from Mentor Graphics for indicating to us some recent results on quantized neural networks, Dr. Alan Mishchenko from UC Berkeley for his kind suggestions and support regarding \(\texttt {ABC}\), and Hugo A. Andrade from Xilinx for exchanging the view of BNN.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Chih-Hong Cheng
    • 1
    Email author
  • Georg Nührenberg
    • 1
  • Chung-Hao Huang
    • 1
  • Harald Ruess
    • 1
  1. 1.fortiss - Landesforschungsinstitut des Freistaats BayernMunichGermany

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