Digital Implementation of Oscillatory Neural Network for Image Recognition Application

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 both the academic and industrial community investi- gating novel computing paradigms and design approaches at all levels from materials, devices, circuits, architectures, and all the way to system-level im- plementations and applications. For example, to improve performance, the community has been investigating solutions through massively parallel and distributed systems that are a rupture from von Neumann architectures. As artificial neural networks (ANN) and deep neural networks (DNN) that are trained over hundreds of graphic processing units (GPU)-accelerated servers where each GPU can have thousands of cores. The limitations of data pro- cessing through the memory wall in von Neumann architectures have been overcome with bringing computing to the data inspired by biological brain- like computing. An alternative computing approach based on ANNs uses oscillators to compute or oscillatory neural networks (ONNs). Such an approach differs from classical CMOS and classical von Neumann where building blocks are analog and perform computations efficiently. Moreover, data is encoded on the oscillator signals phase, which is a departure from the classical voltage level based data encoding (such as amplitude voltage to represent a logical bit ’1’ or ’0’). ONNs can perform computations efficiently and can be used to build a more extensive neuromorphic system. How should a designer choose to optimally implement ONNs in analog is the focus of many ongoing re- search efforts. But here, we address a more fundamental problem, can we efficiently perform AI applications (such as image/pattern recognition) with ONNs? In other words, what are the advantages and limitations of the ONN computing paradigm for practical AI applications? Here, we present a dig- ital 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 5x3 and 10x6 ONNs. We present the digital ONN imple- mentation 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.