  Deep Neural Networks (DNNs) are fast becoming ubiquitous for their ability to
attain good accuracy in various machine learning tasks. A DNN's architecture
(i.e., its hyper-parameters) broadly determines the DNN's accuracy and
performance, and is often confidential. Attacking a DNN in the cloud to obtain
its architecture can potentially provide major commercial value. Further,
attaining a DNN's architecture facilitates other, existing DNN attacks.
  This paper presents Cache Telepathy: a fast and accurate mechanism to steal a
DNN's architecture using the cache side channel. Our attack is based on the
insight that DNN inference relies heavily on tiled GEMM (Generalized Matrix
Multiply), and that DNN architecture parameters determine the number of GEMM
calls and the dimensions of the matrices used in the GEMM functions. Such
information can be leaked through the cache side channel.
  This paper uses Prime+Probe and Flush+Reload to attack VGG and ResNet DNNs
running OpenBLAS and Intel MKL libraries. Our attack is effective in helping
obtain the architectures by very substantially reducing the search space of
target DNN architectures. For example, for VGG using OpenBLAS, it reduces the
search space from more than $10^{35}$ architectures to just 16.
