Name
Abstract | ||
|---|---|---|
We present an architecture and a compilation toolchain for stochastic machines dedicated to Bayesian inferences. These machines are not Von Neumann and code information with stochastic bitstreams instead of using floating point representations. They only rely on stochastic arithmetic and on Gibbs sampling to perform approximate inferences. They use banks of binary random generators which capture t... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TETC.2016.2609926 | IEEE Transactions on Emerging Topics in Computing |
Keywords | Field | DocType |
Bayes methods,Computer architecture,Probabilistic logic,Hardware,Logic gates,Correlation,Programming | Stochastic optimization,Joint probability distribution,Inference,Computer science,Theoretical computer science,Bayesian statistics,Stochastic computing,Gibbs sampling,Von Neumann architecture,Bayesian probability | Journal |
Volume | Issue | ISSN |
7 | 1 | 2168-6750 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marvin Faix | 1 | 1 | 0.36 |
| Raphaël Laurent | 2 | 2 | 1.11 |
| Pierre Bessière | 3 | 425 | 86.40 |
| Emmanuel Mazer | 4 | 272 | 58.70 |
| Jacques Droulez | 5 | 121 | 15.77 |