ArticlePhysical review applied2020
Energy-efficient stochastic computing with superparamagnetic tunnel junctions.
Article in Physical review applied, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The trial behind it
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Who cites it
11 citing papers in PubMed.
- Hardware Acceleration of Stochastic Neural Networks Enabled by Bit-Cell Level Co-Design of Magnetic Tunnel Junctions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Emergent Chaos-Like Dynamics of Spin-Orbit-Torque-Driven Magnetic Transitions.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Advantages of imperfect dice rolls over coin flips for random number generation.Scientific reports · 2025Article
- Superior probabilistic computing using operationally stable probabilistic-bit constructed by a manganite nanowire.National science review · 2025Article
- Hardware implementation of Bayesian network based on two-dimensional memtransistors.Nature communications · 2022Article
- Entropy considerations in improved circuits for a biologically-inspired random pulse computer.Scientific reports · 2022Article
- Control of the stochastic response of magnetization dynamics in spin-torque oscillator through radio-frequency magnetic fields.Scientific reports · 2021Article
- Mutual control of stochastic switching for two electrically coupled superparamagnetic tunnel junctions.Physical review. B · 2021Article
- Comparison of measured and simulated spin-wave mode spectra of magnetic nanostructures.Applied physics letters · 2021Article
- Noise suppression beyond the thermal limit with nanotransistor biosensors.Scientific reports · 2020Article
- Electrically programmable probabilistic bit anti-correlator on a nanomagnetic platform.Scientific reports · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Superparamagnetic tunnel junctions (SMTJs) have emerged as a competitive, realistic nanotechnology to support novel forms of stochastic computation in CMOS-compatible platforms. One of their applications is to generate random bitstreams suitable for use in stochastic computing implementations. We describe a method for digitally programmable bitstream generation based on pre-charge sense amplifiers. This generator is significantly more energy efficient than SMTJ-based bitstream generators that tune probabilities with spin currents and a factor of two more efficient than related CMOS-based implementations. The true randomness of this bitstream generator allows us to use them as the fundamental units of a novel neural network architecture. To take advantage of the potential savings, we codesign the algorithm with the circuit, rather than directly transcribing a classical neural network into hardware. The flexibility of the neural network mathematics allows us to adapt the network to the explicitly energy efficient choices we make at the device level. The result is a convolutional neural network design operating at ≈ 150 nJ per inference with 97 % performance on MNIST-a factor of 1.4 to 7.7 improvement in energy efficiency over comparable proposals in the recent literature.
Identifiers
33043097PMC7542576What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.