Hardware Implementation of Low-Power Low-Area Programmable Interval Type-2 Fuzzifier

2018 ◽  
Author(s):  
Rouhollah Mohammadi Nasr ◽  
Abdollah Khoei
Keyword(s):  
Low Power ◽  
Hardware Implementation ◽  
Low Area ◽  
Interval Type

scholarly journals A PWM Nie-Tan Type-Reducer Circuit for a Low-Power Interval Type-2 Fuzzy Controller

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Gabriel A. F. Souza ◽  
Rodrigo B. Santos ◽  
Lester A. Faria
Keyword(s):  
Low Power ◽  
Fuzzy Controller ◽  
Interval Type

scholarly journals Quantum Tunnelling based Ultra-compact and Energy Efficient Spiking Neuron enables Hardware Liquid State Machine

2021 ◽  
Author(s):  
Ajay Singh ◽  
Vivek Saraswat ◽  
Maryam Shojaei Baghini ◽  
Udayan Ganguly
Keyword(s):  
Low Power ◽  
Large Scale ◽  
Hardware Implementation ◽  
Cmos Technology ◽  
Quantum Tunnelling ◽  
Low Area ◽  
Standby Power ◽  
Similar Area ◽  
First Time ◽  
Lif Neuron

Abstract Low-power and low-area neurons are essential for hardware implementation of large-scale SNNs. Various novel physics based leaky-integrate-and-fire (LIF) neuron architectures have been proposed with low power and area, but are not compatible with CMOS technology to enable brain scale implementation of SNN. In this paper, for the first time, we demonstrate hardware implementation of LSM reservoir using band-to-band-tunnelling (BTBT) based neuron. A low-power thresholding circuit and current-to-voltage converter design are proposed. We further propose a predistortion technique to linearize a nonlinear neuron without any area and power overhead. We establish the equivalence of the proposed neuron with the ideal LIF neuron to demonstrate its versatility. To verify the effect of the proposed neuron, a 36-neuron LSM reservoir is fabricated in GF-45nm PDSOI technology. We achieved 5000x lower energy-per-spike at a similar area, 50x less area at a similar energy-per-spike, and 10x lower standby power at a similar area and energy-per-spike. Such overall performance improvement enables brain scale computing.

Low power membership function generator for interval type-2 fuzzy system

2019 ◽  
Vol 36 (6) ◽  
pp. 6103-6112
Author(s):  
Gabriel A.F. Souza ◽  
Rodrigo B. Santos ◽  
Lester A. Faria
Keyword(s):  
Low Power ◽  
Membership Function ◽  
Fuzzy System ◽  
Function Generator ◽  
Interval Type

Low-Power Current-Mode Interval Type-2 Fuzzy Inference Engine Circuit

2019 ◽  
Vol 66 (7) ◽  
pp. 2639-2650 ◽  
Author(s):  
Gabriel Antonio Fanelli de Souza ◽  
Rodrigo Bispo dos Santos ◽  
Lester de Abreu Faria
Keyword(s):  
Low Power ◽  
Fuzzy Inference ◽  
Current Mode ◽  
Inference Engine ◽  
Fuzzy Inference Engine ◽  
Interval Type

Influence of different fuzzy operators on analytical structure and variable gains of typical interval type-2 fuzzy PI controller

2020 ◽  
Vol 39 (3) ◽  
pp. 4319-4329
Author(s):  
Haibo Zhou ◽  
Chaolong Zhang ◽  
Shuaixia Tan ◽  
Yu Dai ◽  
Ji’an Duan ◽  
...  
Keyword(s):  
Pi Controller ◽  
Real Time Control ◽  
Fuzzy Operators ◽  
Fuzzy Operator ◽  
Analytical Structure ◽  
Product Operator ◽  
Pi Controllers ◽  
Fuzzy Pi Controller ◽  
Interval Type

The fuzzy operator is one of the most important elements affecting the control performance of interval type-2 (IT2) fuzzy proportional-integral (PI) controllers. At present, the most popular fuzzy operators are product fuzzy operator and min() operator. However, the influence of these two different types of fuzzy operators on the IT2 fuzzy PI controllers is not clear. In this research, by studying the derived analytical structure of an IT2 fuzzy PI controller using typical configurations, it is proved mathematically that the variable gains, i.e., proportional and integral gains of typical IT2 fuzzy PI controllers using the min() operator are smaller than those using the product operator. Moreover, the study highlights that unlike the controllers based on the product operator, the controllers based on the min() operator have a simple analytical structure but provide more control laws. Real-time control experiments on a linear motor validate the theoretical results.

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