Weight Update Generation Circuit Utilizing Phase Noise of Integrated Complementary Metal–Oxide–Semiconductor Ring Oscillator for Memristor Crossbar Array Neural Network‐Based Stochastic Learning. (27th March 2020)
- Record Type:
- Journal Article
- Title:
- Weight Update Generation Circuit Utilizing Phase Noise of Integrated Complementary Metal–Oxide–Semiconductor Ring Oscillator for Memristor Crossbar Array Neural Network‐Based Stochastic Learning. (27th March 2020)
- Main Title:
- Weight Update Generation Circuit Utilizing Phase Noise of Integrated Complementary Metal–Oxide–Semiconductor Ring Oscillator for Memristor Crossbar Array Neural Network‐Based Stochastic Learning
- Authors:
- Bae, Woorham
Yoon, Kyung Jean - Abstract:
- Abstract : Herein, a robust programmable stochastic weight generation method for a memristive neural network is proposed. There have been few prior algorithm suggestions for crossbar neural network‐based stochastic learning; however, there has not been much attention focussed on robust physical implementations. As a result, coming up with a robust method to provide the probability generator is an essential knob for its physical implementation. Here, implanting such stochastic behavior into the weight update signal itself is proposed, by multiplying it with the randomized probability sequence. To generate such probability sequence, bang‐bang dithering of a phase‐locked loop (PLL) with a binary phase detector (PD) is used. The programmable probability is enabled by introducing an offset for the PD outputs. Yet the dithering sequence has deterministic nature, phase noise of complementary metal–oxide–semiconductor (CMOS) ring oscillator to randomize the deterministic dithering is exploited. As a result, this lower power oscillator offers a better probability sequence, which enables an ultralow power circuit implementation. Abstract : A probability generator dedicated for stochastic learning in memristor cross‐point array‐based neural network is presented. It mainly utilizes the intrinsic noise of a complementary metal–oxide–semiconductor (CMOS) ring oscillator, which not only benefits from a robust and practical solution but also from an ultralow power consumption in comparisonAbstract : Herein, a robust programmable stochastic weight generation method for a memristive neural network is proposed. There have been few prior algorithm suggestions for crossbar neural network‐based stochastic learning; however, there has not been much attention focussed on robust physical implementations. As a result, coming up with a robust method to provide the probability generator is an essential knob for its physical implementation. Here, implanting such stochastic behavior into the weight update signal itself is proposed, by multiplying it with the randomized probability sequence. To generate such probability sequence, bang‐bang dithering of a phase‐locked loop (PLL) with a binary phase detector (PD) is used. The programmable probability is enabled by introducing an offset for the PD outputs. Yet the dithering sequence has deterministic nature, phase noise of complementary metal–oxide–semiconductor (CMOS) ring oscillator to randomize the deterministic dithering is exploited. As a result, this lower power oscillator offers a better probability sequence, which enables an ultralow power circuit implementation. Abstract : A probability generator dedicated for stochastic learning in memristor cross‐point array‐based neural network is presented. It mainly utilizes the intrinsic noise of a complementary metal–oxide–semiconductor (CMOS) ring oscillator, which not only benefits from a robust and practical solution but also from an ultralow power consumption in comparison to the methods proposed in prior works. … (more)
- Is Part Of:
- Advanced intelligent systems. Volume 2:Number 5(2020)
- Journal:
- Advanced intelligent systems
- Issue:
- Volume 2:Number 5(2020)
- Issue Display:
- Volume 2, Issue 5 (2020)
- Year:
- 2020
- Volume:
- 2
- Issue:
- 5
- Issue Sort Value:
- 2020-0002-0005-0000
- Page Start:
- n/a
- Page End:
- n/a
- Publication Date:
- 2020-03-27
- Subjects:
- crossbar neural networks -- memristors -- oscillator phase noise -- stochastic learning -- weight generation circuits
Artificial intelligence -- Periodicals
Robotics -- Periodicals
Control theory -- Periodicals
006.3 - Journal URLs:
- http://onlinelibrary.wiley.com/ ↗
https://onlinelibrary.wiley.com/journal/26404567 ↗ - DOI:
- 10.1002/aisy.202000011 ↗
- Languages:
- English
- ISSNs:
- 2640-4567
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 14121.xml