Designing networks of resistively-coupled stochastic Magnetic Tunnel Junctions for energy-based optimum search

We study recurrent networks of binary stochastic Magnetic Tunnel Junctions (sMTJ), aiming at efficiently solving computationally hard optimization problems. After validating a prototyping route, we investigate the impact of hybrid CMOS+MTJ building block variants on the quality of stochastic sampling, a key feature for optimum search in a complex landscape. In this regard, a better decoupling of the read/write paths gives spin-orbit torque (SOT) sMTJs an advantage over two-terminal spin-transfer torque (STT) sMTJs. We carry out a functional and power consumption analysis on asynchronous Ising networks in which coupling occurs through arrays of resistors, in the frame of Boolean satisfiability (SAT) solving. Using our SPICE model, we demonstrate that a 48-node SOT sMTJs network successfully converges to its ground state, factoring an 8-bit integer in 10μs with an estimated power consumption of 133μW/node.