CMOS + stochastic nanomagnets: heterogeneous computers for probabilistic inference and learning

Abstract

Extending Moore's law by augmenting complementary-metal-oxide semiconductor (CMOS) transistors with emerging nanotechnologies (X) has become increasingly important. One important class of problems involve sampling-based Monte Carlo algorithms used in probabilistic machine learning, optimization, and quantum simulation. Here, we combine stochastic magnetic tunnel junction (sMTJ)-based probabilistic bits (p-bits) with Field Programmable Gate Arrays (FPGA) to create an energy-efficient CMOS + X (X = sMTJ) prototype. This setup shows how asynchronously driven CMOS circuits controlled by sMTJs can perform probabilistic inference and learning by leveraging the algorithmic update-order-invariance of Gibbs sampling. We show how the stochasticity of sMTJs can augment low-quality random number generators (RNG). Detailed transistor-level comparisons reveal that sMTJ-based p-bits can replace up to 10,000 CMOS transistors while dissipating two orders of magnitude less energy. Integrated versions of our approach can advance probabilistic computing involving deep Boltzmann machines and other energy-based learning algorithms with extremely high throughput and energy efficiency.

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Cited by in corpus (22)

• Experimental demonstration of an integrated on-chip p-bit core utilizing stochastic Magnetic Tunnel Junctions and 2D-MoS2 FETs
• Is stochastic thermodynamics the key to understanding the energy costs of computation?
• Integrated probabilistic computer using voltage-controlled magnetic tunnel junctions as its entropy source
• ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers
• Voltage-insensitive stochastic magnetic tunnel junctions with double free layers
• Double-Free-Layer Stochastic Magnetic Tunnel Junctions with Synthetic Antiferromagnets
• Noise-augmented Chaotic Ising Machines for Combinatorial Optimization and Sampling
• Scalable Connectivity for Ising Machines: Dense to Sparse
• Heisenberg machines with programmable spin-circuits
• Pushing the Boundary of Quantum Advantage in Hard Combinatorial Optimization with Probabilistic Computers
• Superior probabilistic computing using operationally stable probabilistic-bit constructed by manganite nanowire
• Engineering-Oriented Design of Drift-Resilient MTJ Random Number Generator via Hybrid Control Strategies
• Connecting physics to systems with modular spin-circuits
• Lagrange Oscillatory Neural Networks for Constraint Satisfaction and Optimization
• Superparamagnetic and Stochastic-Write Magnetic Tunnel Junctions for High-Speed True Random Number Generation in Advanced Computing
• Application of Probabilistic-bit in Precision Measurements
• Many-body computing on Field Programmable Gate Arrays
• Ground-State Probabilistic Logic with the Simplest Binary Energy Landscape for Probabilistic Computing
• Metrics for spin-based computing
• Self-Adaptive Ising Machines for Constrained Optimization
• Sub-universal variational circuits for combinatorial optimization problems
• Probabilistic Greedy Algorithm Solver Using Magnetic Tunneling Junctions for Traveling Salesman Problem