Linear programming is required in a wide variety of application including routing, scheduling, and various optimization problems. The primal-dual interior point (PDIP) method is state-of-the-art algorithm for solving linear programs, and can be decomposed to matrix-vector multiplication and solving systems of linear equations, both of which can be conducted by the emerging memristor crossbar technique in O(1) time complexity in the analog domain. This work is the first to apply memristor crossbar for linear program solving based on the PDIP method, which has been reformulated for memristor crossbars to compute in the analog domain. The proposed...
The Memristor is a newly synthesized circuit element correlating differences in electrical charge an...
Integer linear programming (ILP) encompasses a very important class of optimization problems that ar...
A linearized programming method of memristor-based neural weights is proposed. Memristor is known as...
Boolean matrix multiplication (BMM) is a fundamental problem with applications in graph theory, grou...
Memristor crossbar is prevailing as one of the most promising candidates to construct the neural net...
This article introduces a new class of memristor neural networks (NNs) for solving, in real-time, qu...
Problems involving discrete Markov Chains are solved mathematically using matrix methods. Recently, ...
The interior point method (IPM) is now well established as a competitive technique for solving very ...
Recently, an in-memory analog circuit based on crosspoint memristor arrays was reported, which enabl...
A new approach for the implementation of interior-point methods for solving linear programs is propo...
With Moore\u27s law approaching physical limitations of transistor size, researchers have started ex...
The recent emerging memristor can provide non-volatile memory storage but also intrinsic computing f...
Abstract — This paper discusses implementations of gradient-descent based learning algorithms on mem...
Conventional digital computers can execute advanced operations by a sequence of elementary Boolean f...
Moore’s law is at its frontier to further increase transistor number on square cm of a chip. Memrist...
The Memristor is a newly synthesized circuit element correlating differences in electrical charge an...
Integer linear programming (ILP) encompasses a very important class of optimization problems that ar...
A linearized programming method of memristor-based neural weights is proposed. Memristor is known as...
Boolean matrix multiplication (BMM) is a fundamental problem with applications in graph theory, grou...
Memristor crossbar is prevailing as one of the most promising candidates to construct the neural net...
This article introduces a new class of memristor neural networks (NNs) for solving, in real-time, qu...
Problems involving discrete Markov Chains are solved mathematically using matrix methods. Recently, ...
The interior point method (IPM) is now well established as a competitive technique for solving very ...
Recently, an in-memory analog circuit based on crosspoint memristor arrays was reported, which enabl...
A new approach for the implementation of interior-point methods for solving linear programs is propo...
With Moore\u27s law approaching physical limitations of transistor size, researchers have started ex...
The recent emerging memristor can provide non-volatile memory storage but also intrinsic computing f...
Abstract — This paper discusses implementations of gradient-descent based learning algorithms on mem...
Conventional digital computers can execute advanced operations by a sequence of elementary Boolean f...
Moore’s law is at its frontier to further increase transistor number on square cm of a chip. Memrist...
The Memristor is a newly synthesized circuit element correlating differences in electrical charge an...
Integer linear programming (ILP) encompasses a very important class of optimization problems that ar...
A linearized programming method of memristor-based neural weights is proposed. Memristor is known as...