.png){kind=icon}
返回
Efficient Memristive Stochastic Differential Equation Solver
.png){kind=icon}
OA
AI
DOI：10.1002/aisy.202300008.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Herein, an efficient numerical solver for stochastic differential equations based on memristors is presented. The solver utilizes the stochastic switching effect in memristive devices to simulate the generation of a Brownian path and employs iterative Euler method computations within memristive crossbars. The correctness of the solution paths generated by the system is examined by solving the Black-Scholes equations and comparing the paths to analytical solutions. It is found that the absolute error of a 128-step path is limited to an order of 10 -2 . Herein, the tolerance of the system is also accessed to crossbar nonidealities by comparing the numerical and analytical paths' variation in error. The numerical solver is sensitive to the variation in operating conditions, with the error increasing by 1.17x , 38.7x , and 1222x as the ambient temperature, wire resistance, and stuck probability of the memristor increase to extreme conditions. The solver is tested on a variety of problems to show its utility for different calculations. And, the resource consumption of the proposed structure built with existing technology is estimated and it is compared with similar iterative solvers. The solver generates a solution with the same level of accuracy from 4x to 10x faster than similar digital or mixed-signal designs.
Keyword:
Black-Scholes equation
Brownian path
memristor crossbar
stochastic differential equation
vector-matrix multiplication
期刊
IF:
6.1
论文数:
2.0K
被引数:
8.4K
机构
引用论文
.png){kind=icon}
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
OAAI
OAAI分享收藏分享收藏
查看更多内容