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Reconstruct the magnetic computing review
A cross-layer reconstruction linking magnetic device physics, probabilistic bits, algorithms, and benchmark evidence without mixing experiments and simulations.
Prepare a source-critical technical report on magnetic probabilistic computing, spanning stochastic MTJs, spin-torque and spin-Hall oscillators, SOT devices, magnetic Ising machines, domain-wall devices, skyrmions, and p-bits. Explain device physics, thermal or driven stochasticity, probability tuning, read/write circuitry, coupling, and how physical states implement sampling, Bayesian inference, optimization, or neuromorphic workloads.
Separate fabricated-device measurements, circuit or FPGA demonstrations, micromagnetic or macrospin simulation, analytical estimates, and proposed architectures. For each result record device stack and geometry, temperature, bias and pulse conditions, array size, CMOS interface, randomness and correlation tests, endurance, yield or variability, and workload definition. Normalize energy per sample or useful operation, throughput or samples/s, latency, area, accuracy or solution quality, and include peripheral, communication, calibration, and host-compute overhead where available.
Deliver a physics-to-algorithm map, evidence-level table, p-bit transfer-function comparison, device and system benchmark matrix, workload-specific results, scaling and variability analysis, and open-problem register. Flag incompatible denominators, distinguish intrinsic switching energy from wall-plug system energy, avoid extrapolating simulation to silicon, and date every state-of-the-art claim.
Focus on p-bit physics
Changes only the device axis to tunable stochastic transfer functions and randomness quality.
Prepare a source-critical technical report on magnetic probabilistic computing, spanning stochastic MTJs, spin-torque and spin-Hall oscillators, SOT devices, magnetic Ising machines, domain-wall devices, skyrmions, and p-bits. Explain device physics, thermal or driven stochasticity, probability tuning, read/write circuitry, coupling, and how physical states implement sampling, Bayesian inference, optimization, or neuromorphic workloads.
Separate fabricated-device measurements, circuit or FPGA demonstrations, micromagnetic or macrospin simulation, analytical estimates, and proposed architectures. For each result record device stack and geometry, temperature, bias and pulse conditions, array size, CMOS interface, randomness and correlation tests, endurance, yield or variability, and workload definition. Normalize energy per sample or useful operation, throughput or samples/s, latency, area, accuracy or solution quality, and include peripheral, communication, calibration, and host-compute overhead where available.
Deliver a physics-to-algorithm map, evidence-level table, p-bit transfer-function comparison, device and system benchmark matrix, workload-specific results, scaling and variability analysis, and open-problem register. Flag incompatible denominators, distinguish intrinsic switching energy from wall-plug system energy, avoid extrapolating simulation to silicon, and date every state-of-the-art claim.
Focus on algorithm mapping
Changes only the evidence axis to how device networks implement defined sampling and Ising workloads.
Prepare a source-critical technical report on magnetic probabilistic computing, spanning stochastic MTJs, spin-torque and spin-Hall oscillators, SOT devices, magnetic Ising machines, domain-wall devices, skyrmions, and p-bits. Explain device physics, thermal or driven stochasticity, probability tuning, read/write circuitry, coupling, and how physical states implement sampling, Bayesian inference, optimization, or neuromorphic workloads.
Separate fabricated-device measurements, circuit or FPGA demonstrations, micromagnetic or macrospin simulation, analytical estimates, and proposed architectures. For each result record device stack and geometry, temperature, bias and pulse conditions, array size, CMOS interface, randomness and correlation tests, endurance, yield or variability, and workload definition. Normalize energy per sample or useful operation, throughput or samples/s, latency, area, accuracy or solution quality, and include peripheral, communication, calibration, and host-compute overhead where available.
Deliver a physics-to-algorithm map, evidence-level table, p-bit transfer-function comparison, device and system benchmark matrix, workload-specific results, scaling and variability analysis, and open-problem register. Flag incompatible denominators, distinguish intrinsic switching energy from wall-plug system energy, avoid extrapolating simulation to silicon, and date every state-of-the-art claim.
Audit energy and throughput
Changes only the metric axis to denominator-consistent system benchmarking.
Prepare a source-critical technical report on magnetic probabilistic computing, spanning stochastic MTJs, spin-torque and spin-Hall oscillators, SOT devices, magnetic Ising machines, domain-wall devices, skyrmions, and p-bits. Explain device physics, thermal or driven stochasticity, probability tuning, read/write circuitry, coupling, and how physical states implement sampling, Bayesian inference, optimization, or neuromorphic workloads.
Separate fabricated-device measurements, circuit or FPGA demonstrations, micromagnetic or macrospin simulation, analytical estimates, and proposed architectures. For each result record device stack and geometry, temperature, bias and pulse conditions, array size, CMOS interface, randomness and correlation tests, endurance, yield or variability, and workload definition. Normalize energy per sample or useful operation, throughput or samples/s, latency, area, accuracy or solution quality, and include peripheral, communication, calibration, and host-compute overhead where available.
Deliver a physics-to-algorithm map, evidence-level table, p-bit transfer-function comparison, device and system benchmark matrix, workload-specific results, scaling and variability analysis, and open-problem register. Flag incompatible denominators, distinguish intrinsic switching energy from wall-plug system energy, avoid extrapolating simulation to silicon, and date every state-of-the-art claim.