  Despite foreseeing tremendous speedups over conventional deep neural
networks, the performance advantage of binarized neural networks (BNNs) has
merely been showcased on general-purpose processors such as CPUs and GPUs. In
fact, due to being unable to leverage bit-level-parallelism with a word-based
architecture, GPUs have been criticized for extremely low utilization (1%) when
executing BNNs. Consequently, the latest tensorcores in NVIDIA Turing GPUs
start to experimentally support bit computation. In this work, we look into
this brand new bit computation capability and characterize its unique features.
We show that the stride of memory access can significantly affect performance
delivery and a data-format co-design is highly desired to support the
tensorcores for achieving superior performance than existing software solutions
without tensorcores. We realize the tensorcore-accelerated BNN design,
particularly the major functions for fully-connect and convolution layers --
bit matrix multiplication and bit convolution. Evaluations on two NVIDIA Turing
GPUs show that, with ResNet-18, our BTC-BNN design can process ImageNet at a
rate of 5.6K images per second, 77% faster than state-of-the-art. Our BNN
approach is released on https://github.com/pnnl/TCBNN.
