Probabilistic Circuit Architecture Using Statistical Learning

The main achievement of this project is the generalization of probabilistic circuit architecture. In other words, in mapping MRF into CMOS circuitry, one must fulfill two requirements; first bistable storage element for each logic state and second feedback network for belief propagation

ENHANCEMENT OF MARKOV RANDOM FIELD MECHANISM TO ACHIEVE FAULT-TOLERANCE IN NANOSCALE CIRCUIT DESIGN

As the MOSFET dimensions scale down towards nanoscale level, the reliability of circuits based on these devices decreases. Hence, designing reliable systems using these nano-devices is becoming challenging. Therefore, a mechanism has to be devised that can make the nanoscale systems perform reliably using unreliable circuit components. The solution is fault-tolerant circuit design. Markov Random Field (MRF) is an effective approach that achieves fault-tolerance in integrated circuit design. The previous research on this technique suffers from limitations at the design, simulation and implementation levels. As improvements, the MRF fault-tolerance rules have been validated for a practical circuit example. The simulation framework is extended from thermal to a combination of thermal and random telegraph signal (RTS) noise sources to provide a more rigorous noise environment for the simulation of circuits build on nanoscale technologies. Moreover, an architecture-level improvement has been proposed in the design of previous MRF gates. The redesigned MRF is termed as Improved-MRF. The CMOS, MRF and Improved-MRF designs were simulated under application of highly noisy inputs. On the basis of simulations conducted for several test circuits, it is found that Improved-MRF circuits are 400 whereas MRF circuits are only 10 times more noise-tolerant than the CMOS alternatives. The number of transistors, on the other hand increased from a factor of 9 to 15 from MRF to Improved-MRF respectively (as compared to the CMOS). Therefore, in order to provide a trade-off between reliability and the area overhead required for obtaining a fault-tolerant circuit, a novel parameter called as ‘Reliable Area Index’ (RAI) is introduced in this research work. The value of RAI exceeds around 1.3 and 40 times for MRF and Improved-MRF respectively as compared to CMOS design which makes Improved- MRF to be still 30 times more efficient circuit design than MRF in terms of maintaining a suitable trade-off between reliability and area-consumption of the circuit

Physically Equivalent Intelligent Systems for Reasoning Under Uncertainty at Nanoscale

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

Mathematical Estimation of Logical Masking Capability of Majority/Minority Gates Used in Nanoelectronic Circuits

In nanoelectronic circuit synthesis, the majority gate and the inverter form the basic combinational logic primitives. This paper deduces the mathematical formulae to estimate the logical masking capability of majority gates, which are used extensively in nanoelectronic digital circuit synthesis. The mathematical formulae derived to evaluate the logical masking capability of majority gates holds well for minority gates, and a comparison with the logical masking capability of conventional gates such as NOT, AND/NAND, OR/NOR, and XOR/XNOR is provided. It is inferred from this research work that the logical masking capability of majority/minority gates is similar to that of XOR/XNOR gates, and with an increase of fan-in the logical masking capability of majority/minority gates also increases