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ORCID
N/A
Access Type
Open Access Thesis
Document Type
thesis
Degree Program
Electrical & Computer Engineering
Degree Type
Master of Science (M.S.)
Year Degree Awarded
2017
Month Degree Awarded
September
Abstract
Probabilistic graphical models like Bayesian Networks (BNs) are powerful artificial-intelligence formalisms, with similarities to cognition and higher order reasoning in the human brain. These models have been, to great success, applied to several challenging real-world applications. Use of these formalisms to a greater set of applications is impeded by the limitations of the currently used software-based implementations. New emerging-technology based circuit paradigms which leverage physical equivalence, i.e., operating directly on probabilities vs. introducing layers of abstraction, promise orders of magnitude increase in performance and efficiency of BN implementations, enabling networks with millions of random variables. While majority of applications with small network size (100s of nodes) require only single digit precision for accurate results, applications with larger size (1000s to millions of nodes) require higher precision computation. We introduce a new BN integrated circuit fabric based on mixed-signal magneto-electric circuits which perform probabilistic computations based on the principle of approximate computation. Precision scaling in this fabric is logarithmic in area vs. linear in prior directions. Results show 33x area benefit for a 0.001 precision compared to prior direction, while maintaining three orders of magnitude performance benefits vs. 100-core processor implementations.
DOI
https://doi.org/10.7275/10691263
First Advisor
Csaba Anas Moritz
Recommended Citation
Kulkarni, Sourabh, "MAGNETO-ELECTRIC APPROXIMATE COMPUTATIONAL FRAMEWORK FOR BAYESIAN INFERENCE" (2017). Masters Theses. 558.
https://doi.org/10.7275/10691263
https://scholarworks.umass.edu/masters_theses_2/558
Included in
Computational Engineering Commons, Electrical and Computer Engineering Commons, Hardware Systems Commons