Computing paradigm based on von Neuman architectures cannot keep up with the ever-increasing data growth (also called "data deluge gap"). This has resulted in investigating novel computing paradigms and design approaches at all levels from materials to system-level implementations and applications. An alternative computing approach based on artificial neural networks uses oscillators to compute or Oscillatory Neural Networks (ONNs). ONNs can perform computations efficiently and can be used to build a more extensive neuromorphic system. Here, we address a fundamental problem: can we efficiently perform artificial intelligence applications with ONNs? We present a digital ONN implementation to show a proof-of-concept of the ONN approach of "computing-in-phase" for pattern recognition applications. To the best of our knowledge, this is the first attempt to implement an FPGA-based fully-digital ONN. We report ONN accuracy, training, inference, memory capacity, operating frequency, hardware resources based on simulations and implementations of 5 × 3 and 10 × 6 ONNs. We present the digital ONN implementation on FPGA for pattern recognition applications such as performing digits recognition from a camera stream. We discuss practical challenges and future directions in implementing digital ONN.

中文翻译：

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用于图像识别应用的振荡神经网络的数字实现。

基于冯诺依曼架构的计算范式跟不上不断增长的数据增长（也称为“数据洪流缺口”）。这导致在从材料到系统级实现和应用的各个层面研究新颖的计算范式和设计方法。基于人工神经网络的替代计算方法使用振荡器来计算或振荡神经网络 (ONN)。ONN 可以高效地执行计算，并可用于构建更广泛的神经形态系统。在这里，我们解决一个基本问题：我们能否使用 ONN 高效地执行人工智能应用程序？我们提出了一个数字 ONN 实现，以展示用于模式识别应用程序的“同相计算”ONN 方法的概念验证。据我们所知，这是实现基于 FPGA 的全数字 ONN 的首次尝试。我们基于 5 × 3 和 10 × 6 ONN 的模拟和实现报告 ONN 准确性、训练、推理、内存容量、操作频率、硬件资源。我们展示了 FPGA 上的数字 ONN 实现，用于模式识别应用，例如从相机流中执行数字识别。我们讨论了实施数字 ONN 的实际挑战和未来方向。