High-Throughput CNN Inference on Embedded ARM big.LITTLE Multi-Core Processors

IoT Edge intelligence requires Convolutional Neural Network (CNN) inference to take place in the edge device itself. ARM big.LITTLE architecture is at the heart of common commercial edge devices. It comprises of single-ISA heterogeneous multi-cores grouped in homogeneous clusters that enables performance and power trade-offs. However, high communication overhead involved in parallelization of computation from a convolution kernel across clusters is detrimental to throughput. We present an alternative framework called Pipe-it that employs a pipelined design to split the convolutional layers across clusters while limiting the parallelization of their respective kernels to the assigned clusters. We develop a performance prediction model that, from convolutional layer descriptors, predicts the execution time of each layer individually on all different core types and number of cores. Pipe-it then exploits the predictions to create a balanced pipeline using an efficient design space exploration algorithm. Pipe-it on average results in 39% higher throughput than the highest antecedent throughput.