Method and architecture for fuzzy-logic using unary processing

Patent metadata

Field	Value
Publication number	US-12518186-B2
Application number	US-202117340834-A
Country	US
Kind code	B2
Filing date	Jun 7, 2021
Priority date	Jun 8, 2020
Publication date	Jan 6, 2026
Grant date	Jan 6, 2026

Abstract

Efficient hardware design of the fuzzy-inference engine has become necessary for high-performance applications. The disclosed technology applies unary processing to the platform of fuzzy-logic. To mitigate the latency, the proposed design processes right-aligned bit-streams. A one-hot decoder is used for fast detection of the bit-stream with maximum value. Implementing a fuzzy-inference engine with 81 fuzzy-inference rules, the disclosed architecture provides 82%, 46%, and 67% saving in the hardware area, power and energy consumption, respectively, and 94% reduction in the number of used LUTs compared to conventional binary implementation.

First claim

We claim:

1 . An architecture for performing fuzzy interference in unary computing systems, comprising:
a fuzzification module; a unary number generator; a rule-apply module; an aggregation module; and a defuzzification module, comprising:
one or more inputs comprising aggregated values;
a database comprising at least one prestored value;
a one-hot decoder; and
a MUX unit;
wherein the aggregated values comprise one or more outputs from the aggregation module;
wherein the aggregated values further comprise corresponding unary bit-streams; and
wherein the one-hot decoder comprises functionality to detect which unary bit-stream has the maximum value.

2 . The architecture of claim 1 , wherein the unary number generator comprises:
a down counter; and two or more comparators.

3 . The architecture of claim 1 , wherein the unary number generator consists of:
one down counter; and two or more comparators.

4 . A method for performing fuzzy interference in unary computing systems, comprising:
a. providing an architecture, comprising:
a fuzzification module;
a unary number generator
a rule-apply module;
an aggregation module; and
a defuzzification module;
b. performing a fuzzification process by the fuzzification module, comprising converting one or more crisp inputs to one or more fuzzy linguistic values; c. converting numbers in a range of [0,1] of the fuzzy linguistic values to unary bit-streams by the unary number generator; d. applying one or more inference rules by the rule-apply module; e. aggregating one or more outputs of the rule-apply module; and f. performing a defuzzification process by the defuzzification module, wherein the fuzzy linguistic values are converted to one or more crisp value outputs.

5 . The method of claim 4 , wherein the unary number generator comprises:
a down counter; and two or more comparators.

6 . The method of claim 4 , wherein the crisp input conversion is based on an input membership function.

7 . The method of claim 4 , wherein the unary number generator consists of:
one down counter; and two or more comparators.

8 . The method of claim 4 , wherein the defuzzification module comprises:
one or more inputs comprising aggregated values; a database comprising at least one prestored value; a one-hot decoder; and a MUX unit; wherein the aggregated values comprise one or more outputs from the aggregation module; and wherein the aggregated values further comprise corresponding unary bit-streams.

9 . The method of claim 8 , further comprising:
transmitting the unary bit-streams corresponding the aggregated values to the one-hot decoder; detecting the unary bit-stream with the maximum value by the one-hot decoder; upon the one-hot decoder detecting the maximum value unary bit-stream, a corresponding index of the maximum value unary bit-stream is sent to the MUX unit; and the MUX unit selects a corresponding defuzzification value.

10 . The method of claim 8 , wherein the unary bit-stream with the maximum value comprises the bit-stream that generates a 1 in the respective bit-stream first before the other unary bit-streams.

Assignees

• Univ Louisiana At Lafayette

Inventors

• Najafi Mohammadhassan
• Jalilvand Amir Hossein
• Fazeli Mahdi

Classifications

• G06F16/24554

  Unary operations; Data partitioning operations · CPC title

• G06N7/026

  Development tools for entering the parameters of a fuzzy system · CPC title

• G06N7/023 Primary

  Learning or tuning the parameters of a fuzzy system · CPC title

• G06N5/048 Primary

  Fuzzy inferencing · CPC title

• G06N7/04

  Physical realisation · CPC title