ODENet is a deep neural network architecture in which a stacking structure of ResNet is implemented with an ordinary differential equation (ODE) solver. It can reduce the number of parameters and strike a balance between accuracy and performance by selecting a proper solver. It is also possible to improve the accuracy while keeping the same number of parameters on resource-limited edge devices. In this paper, using Euler method as an ODE solver, a part of ODENet is implemented as a dedicated logic on a low-cost FPGA (Field-Programmable Gate Array) board, such as PYNQ-Z2 board. As ODENet variants, reduced ODENets (rODENets) each of which heavily uses a part of ODENet layers and reduces/eliminates some layers differently are proposed and analyzed for low-cost FPGA implementation. They are evaluated in terms of parameter size, accuracy, execution time, and resource utilization on the FPGA. The results show that an overall execution time of an rODENet variant is improved by up to 2.66 times compared to a pure software execution while keeping a comparable accuracy to the original ODENet.
翻译:ODENet是一个深层的神经网络结构,在其中安装了ResNet的堆叠结构,使用普通的差分方程式(ODE)求解器。它可以减少参数数量,并通过选择一个合适的求解器在准确性和性能之间取得平衡。还可以提高精确性,同时对资源有限的边缘设备保留相同数量的参数。在本文中,使用Euler方法作为ODE解答器,ODENet的一部分被作为低成本的FPGA(外地可配置门阵列)板(如PYNQ-Z2版板)的专用逻辑执行。ODENet变异体中,每个变体都大量使用ODEnets(RODEnets)的某一部分,并减少/消除某些层次,但提议和分析这些变异,以便低成本的FPGGA实施。在参数大小、精度、执行时间和资源利用方面对ODENet进行了评估。结果显示,与纯软件执行相比,RODENet变体的总体执行时间提高到2.66倍。</s>