SpALEn: Sp arsity A ware L oad Balancing Inference E ngine for Neural Network

In the field of computer science, Convolutional Neural Network (CNN) algorithms are a crucial tool that contributes to the advancement of Computer Vision. CNNs are composed of an enormous number of multiplication and addition operations performed on the input data to calculate the probability and predict the output. In multiplication, if any of the operands is zero-valued, then it is irrelevant in that particular output, and hence, these computations can be omitted to avoid unnecessary computation. In this study, we propose an architecture that performs the computations through parallel Processing Elements and is also capable of skipping the ineffectual zero-valued computation to improve PE utilisation. Our proposed work, SpALEn, adopts the channel-first dataflow and is designed to perform the inference function with zero-skipping for enhanced performance. Moreover, due to the skipped computation, load imbalance occurs as the computation workload varies among the PEs. A dynamic logic is designed to mitigate this and ensure the hardware resources are utilised thoroughly. SpALEn achieves a speedup of 12 \ (\) in comparison to a dense architecture.