Low-Power Adiabatic/MTJ LIM-Based XNOR/XOR Synapse and Neuron for Binarized Neural Networks

Using binarized neural network (BNN) as an alternative to the conventional convolutional neural network is a promising candidate to answer the demand of using human brain-inspired in applications with limited hardware and power resources, such as biomedical devices, IoT edge sensors, and other batte...

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Bibliographic Details
Main Authors: Nasab, Milad Tanavardi, Thapliyal, Himanshu
Format: Conference Proceeding
Language:English
Subjects:
Biosensors
Hardware
Memory management
Neurons
Nonvolatile memory
Power demand
Sensors
Online Access:Request full text
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Summary:Using binarized neural network (BNN) as an alternative to the conventional convolutional neural network is a promising candidate to answer the demand of using human brain-inspired in applications with limited hardware and power resources, such as biomedical devices, IoT edge sensors, and other battery-operated devices. Using nonvolatile memory elements like MTJ devices in a LiM-based architecture can eliminate the need to access and use external memory which can significantly reduce the power consumption and area overhead. In addition, by using adiabatic-based designs, a significant part of the consumed power can be recovered to the power source which leads to a huge reduction in power consumption which is vital in applications with limited power and hardware resources. In this paper by using nonvolatile MTJ devices in a LiM architecture and using adiabatic-based circuits, an XNOR/XOR synapse and neuron is proposed. The proposed design offers 97% improvement in comparison with its state-of-the-art counterparts in case of power consumption. Also, it achieves at least 7% lower area compared to other counterparts which makes the proposed design a promising candidate for hardware implementation of BNNs.
ISSN:1944-9380
DOI:10.1109/NANO58406.2023.10231249