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Binarized Neural Networks (BNNs) significantly reduce the computation andmemory demands with binarized weights and activations compared tofull-precision NNs. Executing a layer in a BNN on different devices of aheterogeneous multiprocessor platform consisting of CPU and GPU can affect theinference performance, i.e., accuracy and latency. Usually, a heterogeneous HWplatform consisting of a CPU and a GPU is available to execute the BNNworkloads. However, to use the heterogeneous HW effectively, it is necessary tofind an efficient strategy for BNN workload mapping. In this work, we propose aframework that generates efficient BNN layer-to-device mappings (i.e. suitableparallel configuration for each layer of the model) for execution platformscomprised of CPU and CUDA-capable GPU. We evaluate our proposed framework withtwo BNN architectures using two well-known datasets, Fashion-MNIST andCIFAR-10, on three hardware platforms with different characteristics. Theresults show that compared to running a fully-parallelized GPU implementation,our framework generates an efficient configuration up to 2x, 2.6x and 11.8xfaster on our tested hardware respectively.

HEP-BNN: A Framework for Finding Low-Latency Execution Configurations of BNNs on Heterogeneous Multiprocessor Platforms