In‐Memory Eigenvector Computation in Time O (1)
In‐memory computing with cross‐point resistive memory arrays has gained enormous attention to accelerate the matrix‐vector multiplication in the computation of data‐centric applications. By combining a cross‐point array and feedback amplifiers, it is possible to compute matrix eigenvectors in one step without algorithmic iterations. Herein, time complexity of the eigenvector computation is investigated, based on the feedback analysis of the cross‐point circuit. The results show that the computing time of the circuit is determined by the mismatch degree of the eigenvalues implemented in the circuit, which controls the rising speed of output voltages. For a dataset of random matrices, the time for computing the dominant eigenvector in the circuit is constant for various matrix sizes; namely, the time complexity is O (1). The O (1) time complexity is also supported by simulations of PageRank of real‐world datasets. This work paves the way for fast, energy‐efficient accelerators for eigenvector computation in a wide range of practical applications.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
In‐Memory Eigenvector Computation in Time O (1) ; volume:2 ; number:8 ; year:2020 ; extent:7
Advanced intelligent systems ; 2, Heft 8 (2020) (gesamt 7)
- Creator
- DOI
-
10.1002/aisy.202000042
- URN
-
urn:nbn:de:101:1-2022063006333207949992
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:39 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Sun, Zhong
- Pedretti, Giacomo
- Ambrosi, Elia
- Bricalli, Alessandro
- Ielmini, Daniele
Other Objects (12)
Inner Product Computation In-Memory using Distributed Arithmetic
A Parallel-friendly Majority Gate to Accelerate In-memory Computation
In-Memory Interval Joins
In-memory mechanical computing
In-Memory Interval Joins
In‐Memory Database Query
A course in in-memory data management : the inner mechanics of in-memory databases
A course in in-memory data management : the inner mechanics of in-memory databases
Mixed workload management in-memory databases
Energy-Efficient In-Memory Database Computing
Enterprise-specific in-memory data management : HYRISE c, an in-memory column store engine for OLXP
In-memory Databases in Business Information Systems
Inner Product Computation In-Memory using Distributed Arithmetic
A Parallel-friendly Majority Gate to Accelerate In-memory Computation
In-Memory Interval Joins
In-memory mechanical computing
In-Memory Interval Joins
In‐Memory Database Query
A course in in-memory data management : the inner mechanics of in-memory databases
A course in in-memory data management : the inner mechanics of in-memory databases
Mixed workload management in-memory databases
Energy-Efficient In-Memory Database Computing
Enterprise-specific in-memory data management : HYRISE c, an in-memory column store engine for OLXP
In-memory Databases in Business Information Systems
Inner Product Computation In-Memory using Distributed Arithmetic
A Parallel-friendly Majority Gate to Accelerate In-memory Computation
In-Memory Interval Joins
In-memory mechanical computing
In-Memory Interval Joins
In‐Memory Database Query
A course in in-memory data management : the inner mechanics of in-memory databases
A course in in-memory data management : the inner mechanics of in-memory databases
Mixed workload management in-memory databases
Energy-Efficient In-Memory Database Computing
Enterprise-specific in-memory data management : HYRISE c, an in-memory column store engine for OLXP