The recent advancements in the artificial intelligence field, and the explosion of applications based on Neural Network (NN) deployed in the real world, pose new challenges in optimizing their execution. Edge computing is meant to answer this challenge. Characterized by embedded, low-power System-on-Chips (SoCs) devices, it is a straightforward choice for the NN deployment requirements. However, these devices usually lack specialized hardware to efficiently execute the target workloads. Moreover, the main operations required by NNs are Multiply-And-Accumulate (MAC) operations, with operands at high precision data types and this could lead to a massive number of operations not suitable for SoCs based on CPUs.