LAUSR

Machine learning techniques, particularly those based on neural networks, are always more often used at the edge of the network by Internet of Things (IoT) nodes. Unfortunately, the computation capabilities demanded by those applications, together with their energy efficiency-related constraints, exceed those exposed by embedded general-purpose processors. For this reason, the use of domain-specific hardware accelerators (DSAs) is considered the most viable solution to the unsustainable “Turing tariff” of general-purpose hardware. Starting from the observation that memory and communication traffic account for a large fraction of the overall latency and energy in deep neural network (DNN) inferences, this article proposes a new compression technique aimed at: 1) reducing the memory footprint for storing the model parameters of a DNN and 2) improving DNN inference latency and energy on resource-constrained IoT devices. The proposed compression technique, namely, LineCompress, is applied on a set of representative convolutional neural networks (CNNs) for object recognition mapped on a state-of-the-art DSA targeted for resource-constrained IoT devices. We show that on average, $7.4\times $ memory footprint reduction can be obtained, thus reducing the memory and communication traffic that result to 77% and 87% inference latency and energy reduction, respectively, trading-off efficiency versus accuracy.