An Ultra Lowpower Memristive Neuromorphic Circuit For Internet Of Things Smart Sensors

• doi: 10.1109/JIOT.2018.2799948
• title: An Ultra Low-Power Memristive Neuromorphic Circuit for Internet of Things Smart Sensors
• publisher: IEEE
• isbn:
• issn: 2372-2541
• rank: 1523
• access_type: LOCKED
• content_type: Journals
• abstract: In this paper, we propose an ultra low-power analog neuromorphic circuit to be trained to process sensory data in the Internet of Things smart sensors where low-power and area-efficient computing is required. To reduce the operating voltage of the circuit while maintaining the performance, we focus on designing a memristive neuromorphic circuit without employing operational amplifiers. Therefore, we use the CMOS inverters as the neurons in our memristive neuromorphic circuit. We also propose ultra low-power mixed-signal input/output interfaces to make the circuit connectable to other digital components such as embedded processor. To assess the efficacy of the proposed circuit and its interfaces which include memristive neural network based A/D and D/A converters, HSPICE simulations are utilized. The results indicate that at the operating voltage of ±0.25 V, at least $108 \times $ ( $278 \times $ ) reduction in the power consumption of the output (input) interface compared to that of the conventional structures is achieved. Additionally, the effectiveness of the neuromorphic circuit enhanced by the proposed interfaces is evaluated under some applications such as image recognition, human behavior analysis, and air quality predictions. The results of the study reveal that the designed neuromorphic circuits, along with the proposed A/D and D/A converters, provide an average power saving (speedup) of $2960 \times $ ( $37 \times $ ) over the ASIC implementation in a 90-nm CMOS technology.
• article_number: 8274952
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8274952
• html_url: https://ieeexplore.ieee.org/document/8274952/
• abstract_url: https://ieeexplore.ieee.org/document/8274952/
• publication_title: IEEE Internet of Things Journal
• conference_location:
• conference_dates:
• publication_number: 6488907
• is_number: 8334665
• publication_year: 2018
• publication_date: April 2018
• start_page: 1011
• end_page: 1022
• citing_paper_count: 34
• citing_patent_count: 0
• download_count: 2045
• insert_date: 20180130
• index_terms:
  • ieee_terms:
    • Neuromorphics
    • Memristors
    • Neurons
    • Inverters
    • Biological neural networks
    • Programming
    • Internet of Things
  • author_terms:
    • Internet of Things (IoT)
    • memristor
    • neuromorphic computing
    • smart sensor
  • dynamic_index_terms:
    • Internet Of Things
    • Internet-of-Things
    • Smart Sensors
    • Ultra-low Power
    • Neuromorphic Circuits
    • Neural Network
    • Power Consumption
    • Air Quality
    • Sensor Data
    • Conventional Structure
    • Internet Data
    • NOT Gate
    • NOT-gate
    • CMOS Inverter
    • Impedance
    • Resistivity
    • Artificial Neural Network
    • Neural Network Model
    • Neurons In Layer
    • Feed-forward Network
    • Feedforward Neural Network
    • Analog-to-digital Converter
    • Analog-to-digital
    • Digital-to-analog Converter
    • mHealth
    • Mobile Health
    • Application Programming Interface
    • Programming Interface
    • Input Voltage
    • Power Efficiency
    • Node Voltage
    • Supply Voltage
    • Maximum Resistance
    • Least Significant Bit
    • Low Bit
    • Minimum Resistance
    • Input Neurons
    • Backpropagation Algorithm
    • Analog Output
    • Low Power Consumption
    • Synaptic Weights
• authors:
  • Author Name: Arash Fayyazi
    Affiliation: School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
    Author URL: https://ieeexplore.ieee.org/author/37085778677
    ID: 37085778677
    Order: 1
    Author Affiliations:
    • School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
  • Author Name: Mohammad Ansari
    Affiliation: School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
    Author URL: https://ieeexplore.ieee.org/author/37061857300
    ID: 37061857300
    Order: 2
    Author Affiliations:
    • School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
  • Author Name: Mehdi Kamal
    Affiliation: School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
    Author URL: https://ieeexplore.ieee.org/author/37543656900
    ID: 37543656900
    Order: 3
    Author Affiliations:
    • School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
  • Author Name: Ali Afzali-Kusha
    Affiliation: School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
    Author URL: https://ieeexplore.ieee.org/author/38270948500
    ID: 38270948500
    Order: 4
    Author Affiliations:
    • School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
  • Author Name: Massoud Pedram
    Affiliation: Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA
    Author URL: https://ieeexplore.ieee.org/author/37278158100
    ID: 37278158100
    Order: 5
    Author Affiliations:
    • Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA

Image Sensor

• sensor_type: CMOS
• resolution: Not specified
• dynamic_range: Not specified
• pixel_size: Not specified
• dark_current: Not specified

Optical Data

• focal_length: Not specified
• aperture: Not specified
• field_of_view: Not specified
• distortion: Not specified

Performance Metrics

Applications & Benefits

• cell_imaging: Not specified
• benefits: Enables digital imaging in a variety of applications such as smartphones, medical devices, and automotive systems.

Supporting Organizations

• supported_by: Not specified

Manuscript Details

• publication_date: April 2018

Relevancy Score

• score: 5
• missing_fields:
  • resolution
  • dynamic_range
  • pixel_size
  • dark_current
  • focal_length
  • aperture
  • field_of_view
  • distortion
  • frame_rate
  • signal_to_noise_ratio
  • shutter_speed
  • power_consumption
  • noise
  • cell_imaging
  • supported_by
  • authors
  • publication_date

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