Neuromorphic Hardware Using Simplified Elements and Thin-Film Semiconductor Devices as Synapse Elements - Simulation of Hopfield and Cellular Neural Network -

Kameda, Tomoya; Kimura, Mutsumi; Nakashima, Yasuhiko

doi:10.1007/978-3-319-70136-3_81

Cited by 5 publications

(1 citation statement)

References 9 publications

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“…Neuromorphic computing hardware refers to a hardware system that supports the scale of simulated neural network models and the speed of neural computing. Its initial hardware implementation includes Field Programmable Gate Array (FPGA) [ 9 ], Neuromorphic Chip (Based on ASIC) [ 10 ] and Digital Signal Processor (DSP) [ 11 ]. The core of hardware implementation research is the construction of neural devices, which can be of electronic [ 12 ], optical [ 13 ] and biological [ 14 ] nature.…”

Section: Introductionmentioning

confidence: 99%

Hardware-Based Hopfield Neuromorphic Computing for Fall Detection

Zahid

Ansari

et al. 2020

Sensors

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With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, traditional machine-learning methods for data classification, especially in real time, are computationally intensive. The deployment of Artificial Intelligence algorithms on embedded hardware for fast data classification and accurate fall detection poses a huge challenge in achieving power-efficient embedded systems. Therefore, by exploiting the associative memory feature of Hopfield Neural Network, a hardware module has been designed to simulate the Neural Network algorithm which uses sensor data integration and data classification for recognizing the fall. By adopting the Hebbian learning method for training neural networks, weights of human activity features are obtained and implemented/embedded into the hardware design. Here, the neural network weight of fall activity is achieved through data preprocessing, and then the weight is mapped to the amplification factor setting in the hardware. The designs are checked with validation scenarios, and the experiment is completed with a Hopfield neural network in the analog module. Through simulations, the classification accuracy of the fall data reached 88.9% which compares well with some other results achieved by the software-based machine-learning algorithms, which verify the feasibility of our hardware design. The designed system performs the complex signal calculations of the hardware’s feedback signal, replacing the software-based method. A straightforward circuit design is used to meet the weight setting from the Hopfield neural network, which is maximizing the reusability and flexibility of the circuit design.

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