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Author ORCID Identifier
Open Access Dissertation
Doctor of Philosophy (PhD)
Electrical and Computer Engineering
Year Degree Awarded
Month Degree Awarded
Prof. Csaba Andras Moritz
Prof. Israel Koren
Prof. C. Mani Krishna
Prof. Jayasimha Atulasimha
Computer and Systems Architecture | Nanoscience and Nanotechnology | Other Computer Engineering | VLSI and Circuits, Embedded and Hardware Systems
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 on a probabilistic information representation and multi-domain mixed-signal circuit style, and is tightly coupled to a nanoscale physical layer that spans magnetic and electrical domains. Based on bottom-up device-circuit-architecture simulations, we show up to four orders of magnitude performance improvement (using computational resolution of 0.1) vs. best-of-breed multi-core machines with 100 processors, for BNs with about a million variables. Smaller problem sizes of ~100 variables can be realized at 20 mW power consumption and very low area around a few tenths of a mm2. Our vision is to enable solving complex Bayesian problems in real time, as well as enable intelligence capabilities at a small scale everywhere, ushering in a new era of machine intelligence.
Khasanvis, Santosh, "Physically Equivalent Intelligent Systems for Reasoning Under Uncertainty at Nanoscale" (2015). Doctoral Dissertations. 456.