期刊
FRONTIERS IN NEUROSCIENCE
卷 15, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fnins.2021.713054
关键词
artificial intelligence; auto-associative memory; FPGA implementations; learning rules; oscillatory neural networks; pattern recognition
资金
- European Union's Horizon 2020 research and innovation program, EU H2020 NEURONN project [871501]
This paper explores the potential of using Oscillatory Neural Networks (ONNs) for computing in pattern recognition applications, with a proof-of-concept digital ONN implementation. The researchers report ONN accuracy, training, inference, memory capacity, operating frequency, and hardware resources, and demonstrate the digital ONN implementation on FPGA for pattern recognition tasks.
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 x 3 and 10 x 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.
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