In-Memory Memristive Transformation Stage of Gaussian Random Number Generator

被引：0
|
作者：
Dong, Xuening [1 ]
Amirsoleimani, Amirali [2 ]
Azghadi, Mostafa Rahimi [3 ]
Genov, Roman [1 ]
机构：
[1] Univ Toronto, Dept Elect & Comp Engn, Toronto, ON, Canada
[2] York Univ, Dept Elect Engn & Compute Sci, Toronto, ON M3J 1P3, Canada
[3] James Cook Univ, Coll Sci & Engn, Townsville, Qld, Australia
来源：
2022 IEEE INTERNATIONAL CONFERENCE ON OMNI-LAYER INTELLIGENT SYSTEMS (IEEE COINS 2022) | 2022年
关键词：
Memristor; Crossbar; Vector-Matrix Multiplication; Gaussian Random Number Generator; NORMALITY; POWER;
D O I：
10.1109/COINS54846.2022.9855007
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this work, we present a modification to the digital Wallace-based Gaussian Random Number Generator (GRNG) by implementing an in-memory memristive dot-product engine in place of the vector-matrix multiplication (VMM) stage. The dot-product engine provides an analog interface to the GRNG with statistical robustness and better resource efficiency. One modification with three different structures is proposed and evaluated by the statistical test pass rates and benchmarked against the digital implementations. The best-proposed modification achieved a 95.8% test pass rate for 100 iterative small pool generation while requiring 23.6% and 44.4% less power and area consumption.
引用
收藏
页码：391 / 395
页数：5
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