Spintronics-compatible Approach to Solving Maximum-Satisfiability Problems with Probabilistic Computing, Invertible Logic, and Parallel Tempering

Grimaldi, Andrea; Sánchez-Tejerina, Luis; Aadit, Navid Anjum; Chiappini, Stefano; Carpentieri, Mario; Finocchio, G.

doi:10.1103/physrevapplied.17.024052

Cited by 20 publications

(12 citation statements)

References 44 publications

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“…Future possibilities involve more sophisticated sampling and annealing algorithms such as parallel tempering (PT) (see Ref. [128,129] for some initial investigations). Further improvements to hardware implementation include adaptive versions of PT [130] as well as sophisticated nonequilibrium Monte Carlo (NMC) algorithms [131].…”

Section: Outlook: Algorithms and Applications Beyondmentioning

confidence: 99%

A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

Chowdhury

Grimaldi

Aadit

et al. 2023

IEEE J. Explor. Solid-State Comput. Devices Circuits

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The transistor celebrated its 75 th birthday in 2022. The continued scaling of the transistor defined by Moore's Law continues, albeit at a slower pace. Meanwhile, computing demands and energy consumption required by modern artificial intelligence (AI) algorithms have skyrocketed. As an alternative to scaling transistors for general-purpose computing, the integration of transistors with unconventional technologies has emerged as a promising path for domain-specific computing. In this article, we provide a full-stack review of probabilistic computing with p-bits as a representative example of the energy-efficient and domain-specific computing movement. We argue that p-bits could be used to build energy-efficient probabilistic systems, tailored for probabilistic algorithms and applications. From hardware, architecture, and algorithmic perspectives, we outline the main applications of probabilistic computers ranging from probabilistic machine learning and AI to combinatorial optimization and quantum simulation. Combining emerging nanodevices with the existing CMOS ecosystem will lead to probabilistic computers with orders of magnitude improvements in energy efficiency and probabilistic sampling, potentially unlocking previously unexplored regimes for powerful probabilistic algorithms.

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Section: Outlook: Algorithms and Applications Beyondmentioning

confidence: 99%

A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

Chowdhury

Grimaldi

Aadit

et al. 2023

IEEE J. Explor. Solid-State Comput. Devices Circuits

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“…A hypergraph can be considered as a generalization of graphical data structures wherein an edge (known as a hyperedge) can connect any number of vertices; this is in contrast to a graph where an edge can join a maximum of two vertices. Analog models for solving combinatorial problems in hypergraphs have been relatively less explored [29]- [31]. We note that such problems can, in theory, be reduced to problems that have objective functions with quadratic degree [32], [33].…”

Section: Introductionmentioning

confidence: 99%

Dynamical System-Based Computational Models for Solving Combinatorial Optimization on Hypergraphs

Bashar

Mallick

Ghosh

et al. 2023

IEEE J. Explor. Solid-State Comput. Devices Circuits

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The intrinsic energy minimization in dynamical systems offers a valuable tool for minimizing the objective functions of computationally challenging problems in combinatorial optimization. However, most prior works have focused on mapping such dynamics to combinatorial optimization problems whose objective functions have quadratic degree (e.g., MaxCut); such problems can be represented and analyzed using graphs. However, the work on developing such models for problems that need objective functions with degree greater than two, and subsequently, entail the use of hypergraph data structures, is relatively sparse. In this work, we develop dynamical system-inspired computational models for several such problems. Specifically, we define the 'energy function' for hypergraph-based combinatorial problems ranging from Boolean SAT and its variants to integer factorization, and subsequently, define the resulting system dynamics. We also show that the design approach is applicable to optimization problems with quadratic degree, and use it develop a new dynamical system formulation for minimizing the Ising Hamiltonian. Our work not only expands on the scope of problems that can be directly mapped to, and solved using physicsinspired models, but also creates new opportunities to design highperformance accelerators for solving combinatorial optimization.

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“…Examples include utilizing analog weights encoded in memristors to perform matrix multiplication, [ 17–21 ] implementing stochastic computing to improve the size and energy efficiency of neural networks, [ 22–28 ] as well as using various in‐memory, reservoir, probabilistic, and other physics‐based approaches to computing that capitalize on the unique physics of emerging nanodevices. [ 29–33 ]…”

Section: Introductionmentioning

confidence: 99%

“…Examples include utilizing analog weights encoded in memristors to perform matrix multiplication, [17][18][19][20][21] implementing stochastic computing to improve the size and energy efficiency of neural networks, [22][23][24][25][26][27][28] as well as using various in-memory, reservoir, probabilistic, and other physics-based approaches to computing that capitalize on the unique physics of emerging nanodevices. [29][30][31][32][33] MRAM has emerged as the most promising candidate for many of the above-mentioned embedded applications due to its fast write and read speed, high density (using a onetransistor [12,13] or zero-transistor cell with a two-terminal select device [34] ), high endurance, nonvolatile data retention, and relative ease of integration with existing semiconductor processes, due to the availability of 300 mm volume processing tools.…”

Section: Introductionmentioning

confidence: 99%

Progress and Application Perspectives of Voltage‐Controlled Magnetic Tunnel Junctions

Shao,

Khalili Amiri

2023

Adv Materials Technologies

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This article discusses the current state of development, open research opportunities, and application perspectives of electric‐field‐controlled magnetic tunnel junctions that use the voltage‐controlled magnetic anisotropy effect to control their magnetization. The integration of embedded magnetic random‐access memory (MRAM) into mainstream semiconductor foundry manufacturing opens new possibilities for the development of energy‐efficient, high‐performance, and intelligent computing systems. The current generation of MRAM, which uses the current‐controlled spin‐transfer torque (STT) effect to write information, has gained traction due to its nonvolatile data retention and lower integration cost compared to embedded Flash. However, scaling MRAM to high bit densities will likely require a transition from current‐controlled to voltage‐controlled operation. In this perspective, an overview of voltage‐controlled magnetic anisotropy (VCMA) as a promising beyond‐STT write mechanism for MRAM devices is provided and recent advancements in developing VCMA‐MRAM devices with perpendicular magnetization are highlighted. Starting from the fundamental mechanisms, the key remaining challenges of VCMA‐MRAM, such as increasing the VCMA coefficient, controlling the write error rate, and achieving field‐free VCMA switching are discussed. Then potential solutions are discussed and open research questions are highlighted. Lastly, prospective applications of voltage‐controlled magnetic tunnel junctions (VC‐MTJs) in security applications, extending beyond their traditional role as memory devices are explored.

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Spintronics-compatible Approach to Solving Maximum-Satisfiability Problems with Probabilistic Computing, Invertible Logic, and Parallel Tempering

Cited by 20 publications

References 44 publications

A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

Dynamical System-Based Computational Models for Solving Combinatorial Optimization on Hypergraphs

Progress and Application Perspectives of Voltage‐Controlled Magnetic Tunnel Junctions

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