Adaptive parallel execution of deep neural networks on heterogeneous edge devices

New applications such as smart homes, smart cities, and autonomous vehicles are driving an increased interest in deploying machine learning on edge devices. Unfortunately, deploying deep neural networks (DNNs) on resource-constrained devices presents significant challenges. These workloads are computationally intensive and often require cloud-like resources. Prior solutions attempted to address these challenges by either introducing more design efforts or by relying on cloud resources for assistance. In this paper, we propose a runtime adaptive convolutional neural network (CNN) acceleration framework that is optimized for heterogeneous Internet of Things (IoT) environments. The framework leverages spatial partitioning techniques through fusion of the convolution layers and dynamically selects the optimal degree of parallelism according to the availability of computational resources, as well as network conditions. Our evaluation shows that our framework outperforms state-of-art approaches by improving the inference speed and reducing communication costs while running on wirelessly-connected Raspberry-Pi3 devices. Experimental evaluation shows up to 1.9x ~ 3.7x speedup using 8 devices for three popular CNN models.