Ising-ReRAM: A Low Power Ising Machine ReRAM Crossbar for NP Problems
This addresses the challenge of high power consumption in computational workloads for scalable NP-Complete problem solving, though it appears incremental as it builds on existing physics-inspired methods.
The paper tackled the NP-Complete problem of Boolean satisfiability (SAT) by implementing an Ising model equivalence using a ReRAM crossbar, achieving 91.0% accuracy in matrix representation and demonstrating under linear energy growth for scaling.
Computational workloads are growing exponentially, driving power consumption to unsustainable levels. Efficiently distributing large-scale networks is an NP-Complete problem equivalent to Boolean satisfiability (SAT), making it one of the core challenges in modern computation. To address this, physics and device inspired methods such as Ising systems have been explored for solving SAT more efficiently. In this work, we implement an Ising model equivalence of the 3-SAT problem using a ReRAM crossbar fabricated in the Skywater 130 nm CMOS process. Our ReRAM-based algorithm achieves $91.0\%$ accuracy in matrix representation across iterative reprogramming cycles. Additionally, we establish a foundational energy profile by measuring the energy costs of small sub-matrix structures within the problem space, demonstrating under linear growth trajectory for combining sub-matrices into larger problems. These results demonstrate a promising platform for developing scalable architectures to accelerate NP-Complete problem solving.