Results 51 to 60 of about 263 (134)
Physics‐Grounded Probabilistic Bits for Hardware‐Efficient Intelligent Inference and Optimization
Si–SiNx interface traps are harnessed as a complementary metal–oxide–semiconductor‐compatible source of controllable randomness for probabilistic bits. Pulse‐width‐programmed stochastic capture converts nanoscale defect dynamics into Boltzmann‐consistent binary outputs, while a physics‐based Simulation Program with Integrated Circuit Emphasis model ...
Dokyoung Lee +3 more
wiley +1 more source
Device‐to‐System Perspectives on Scalable Ising Machines for Physics‐Driven Computing
Emerging‐device‐based Ising machines exploit conductance programmability, intrinsic stochasticity, and oscillator dynamics as physical primitives for combinatorial optimization. This review establishes a device‐to‐system framework linking these physical properties to computational functions and crossbar‐based, stochastic p‐bit, and oscillator‐based ...
Sangwook Youn +4 more
wiley +1 more source
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley +1 more source
In‐Memory Continuous‐Time SAT Solver Based on Bidirectional 11‐T SRAM Macro
This article reported a continuous‐time (CT) Boolean satisfiability (SAT) problem solver using bidirectional 11T‐SRAM macro. The proposed system operates asynchronously using capacitor‐based gradient integration and maximizes the parallelism for SAT solving by in‐memory computing (IMC).
Dongseok Kwon +3 more
wiley +1 more source
Computing discrete logarithm by interval-valued paradigm [PDF]
Interval-valued computing is a relatively new computing paradigm. It uses finitely many interval segments over the unit interval in a computation as data structure.
Benedek Nagy, Sándor Vályi
doaj +1 more source
We propose the Powerful‐but‐Limited Generative AI theorem, demonstrating that embedding human‐inspired constraints, such as fixed utility functions and neuro‐symbolic submission layers, ensures generative AI remains controllable by preventing self‐improvement beyond designer intent.
Saeed Banaeian Far +3 more
wiley +1 more source
Generalizing Boolean Satisfiability II: Theory
This is the second of three planned papers describing ZAP, a satisfiability engine that substantially generalizes existing tools while retaining the performance characteristics of modern high performance solvers. The fundamental idea underlying ZAP is that many problems passed to such engines contain rich internal structure that is obscured by the ...
Heidi E. Dixon +3 more
openaire +4 more sources
Finding Minimum‐Cost Explanations for Predictions Made by Tree Ensembles
ABSTRACT The ability to reliably explain why a machine learning model arrives at a particular prediction is crucial when used as decision support by human operators of critical systems. The provided explanations must be provably correct, and preferably without redundant information, called minimal explanations.
John Törnblom +2 more
wiley +1 more source
The Satisfiability Problem for Boolean Set Theory with a Choice Correspondence [PDF]
Given a set U of alternatives, a choice (correspondence) on U is a contractive map c defined on a family Omega of nonempty subsets of U. Semantically, a choice c associates to each menu A in Omega a nonempty subset c(A) of A comprising all elements of A
Domenico Cantone +2 more
doaj +1 more source
This study demonstrates a versatile hardware platform using nano‐oscillators based on binary oxides for deterministic and probabilistic computing. By tailoring material physics, NbOx enables energy‐efficient synchronization for pattern recognition, while enhanced stochasticity in engineered SiOx provides robust entropy for p‐bits to solve complex ...
Jihyun Kim +3 more
wiley +1 more source

