AI 中文总结
本研究提出首款支持复值耦合的全数字振荡神经网络架构ODEONN，其正弦近似方法硬件资源消耗减半，性能下降小于2%，能量延迟积较传统软件模拟降低45倍，适用于ONNs的多种应用。
AI 中文摘要
振荡神经网络（ONNs）是适用于AI与组合优化问题的替代计算范式。然而，数字架构常被设计用于ONNs的特定应用。本研究提出名为ODEONN的模块化、可扩展架构，它适用于ONNs的多种应用，据作者所知，这是首款支持复值耦合的全数字ONN。此外，本研究引入一种正弦函数近似方法，其硬件资源消耗仅为标准方法的一半。将ODEONN的性能与全精度软件模拟对比，显示性能下降小于2%。因此，作者得出结论：定点量化与近似波形对计算精度的影响极小。此外，与在传统硬件上运行的软件模拟相比，ODEONN的能量延迟积降低了45倍。
英文摘要
Oscillatory Neural Networks (ONNs) are an alternative computing paradigm for AI and combinatorial optimization problems. However, digital architectures are often designed for specific applications of ONNs. This work introduces a modular and scalable architecture called ODEONN that is generic to multiple applications of ONNs, and to the best of our knowledge, is the first fully digital ONN to also support complex-valued coupling. Additionally, an approximation of the sine function is introduced that uses half of the hardware resources compared to standard methods. The performance of ODEONN is compared with a full-precision software simulation, where a performance degradation of less than $2\%$ is shown. Therefore, we conclude that the fixed-point quantization and the approximated waveform affect the accuracy of computation by only a small amount. Furthermore, ODEONN shows a 45$\times$ reduction in energy-delay product over the software simulation running on conventional hardware.
Comments23 pages, 6 figures
