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Full-Text Articles in Engineering
Magneto-Electric Approximate Computational Framework For Bayesian Inference, Sourabh Kulkarni
Magneto-Electric Approximate Computational Framework For Bayesian Inference, Sourabh Kulkarni
Masters Theses
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 …
Physically Equivalent Intelligent Systems For Reasoning Under Uncertainty At Nanoscale, Santosh Khasanvis
Physically Equivalent Intelligent Systems For Reasoning Under Uncertainty At Nanoscale, Santosh Khasanvis
Doctoral Dissertations
Machines today lack the inherent ability to reason and make decisions, or operate in the presence of uncertainty. Machine-learning methods such as Bayesian Networks (BNs) are widely acknowledged for their ability to uncover relationships and generate causal models for complex interactions. However, their massive computational requirement, when implemented on conventional computers, hinders their usefulness in many critical problem areas e.g., genetic basis of diseases, macro finance, text classification, environment monitoring, etc. We propose a new non-von Neumann technology framework purposefully architected across all layers for solving these problems efficiently through physical equivalence, enabled by emerging nanotechnology. The architecture builds …