Hardware Estimation for the Eigenvectors of Stochastic Matrices Using Magnetic Tunnel Junctions

Matrices are the foundation of science and engineering. For artificial intelligence (AI) and Internet of Things (IoT) tasks, developing a hardware efficient way to find the eigenvector of stochastic matrix (SM) is urgently in need. In this letter, inspired by the divide-and-conquer strategy, we proposed a new hardware architecture, which uses magnetic tunnel junctions (MTJs) to estimate the eigenvector of an n<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula>n SM where n is the power of 2. This approach reduces the required device amount to log2n by converting the larger SM into 2-state sub-SMs which are further represented by stochastic signals generated by MTJs. The validity of this method has been demonstrated and statistically evaluated. This method provides a novel hardware solution to solve mathematic problems using emerging hardware technologies.