Stochastic Neural Computation I: Computational Elements

By Bradley D. Brown, Howard C. Card. IEEE Trans. Computers, 2001

532 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/bf3b5a94601296ff480a2e528f4b612377b2bdb2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xin Li
2. Michael Orshansky
3. Qianru Zhang
4. Zhifei Sun
5. Meng Zhang
6. Tinghuan Chen
7. Yuzhe Ma
8. Jie Han

In Biomedical Engineering

1. Xin Li
2. Michael Orshansky
3. Jie Han

Citations in the corpus

• Recent Advances in Convolutional Neural Network Acceleration (2018, methodology): “ith random bit streams. It provides many benefits for neural networks such as low computation footprint, error tolerance, simple implementation in circuits and better trade-o between time and accuracy [89]. Many works contribute to exploring potential space in optimization and in deep belief networks”
• Approximate computing: An emerging paradigm for energy-efficient design (2013, background): “Approximate computing, hence, employs deterministic designs that produce imprecise results.”
• An Architecture for Fault-Tolerant Computation with Stochastic Logic (2011, background): “Prior work established specific constructs for such operations [6], [7], [8].”
• Survey of Stochastic Computing (2013, unknown): “Embed.”