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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 …
Function Verification Of Combinational Arithmetic Circuits, Duo Liu
Function Verification Of Combinational Arithmetic Circuits, Duo Liu
Masters Theses
Hardware design verification is the most challenging part in overall hardware design process. It is because design size and complexity are growing very fast while the requirement for performance is ever higher. Conventional simulation-based verification method cannot keep up with the rapid increase in the design size, since it is impossible to exhaustively test all input vectors of a complex design. An important part of hardware verification is combinational arithmetic circuit verification. It draws a lot of attention because flattening the design into bit-level, known as the bit-blasting problem, hinders the efficiency of many current formal techniques. The goal of …