On a Weight Distribution Problem, with Application to the Design of Stochastic Generators
Journal of the ACM (JACM)
Sub-90nm technologies: challenges and opportunities for CAD
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
Design and reliability challenges in nanometer technologies
Proceedings of the 41st annual Design Automation Conference
Designing logic circuits for probabilistic computation in the presence of noise
Proceedings of the 42nd annual Design Automation Conference
Probabilistic system-on-a-chip architectures
ACM Transactions on Design Automation of Electronic Systems (TODAES)
The synthesis of robust polynomial arithmetic with stochastic logic
Proceedings of the 45th annual Design Automation Conference
The synthesis of complex arithmetic computation on stochastic bit streams using sequential logic
Proceedings of the International Conference on Computer-Aided Design
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As CMOS devices are scaled down into the nanometer regime, concerns about reliability are mounting. Instead of viewing nano-scale characteristics as an impediment, technologies such as PCMOS exploit them as a source of randomness. The technology generates random numbers that are used in probabilistic algorithms. With the PCMOS approach, different voltage levels are used to generate different probability values. If many different probability values are required, this approach becomes prohibitively expensive. In this work, we demonstrate a novel technique for synthesizing logic that generates new probabilities from a given set of probabilities. Three different scenarios are considered in terms of whether the given probabilities can be duplicated and whether there is freedom to choose them. In the case that the given probabilities cannot be duplicated and are predetermined, we provide a solution that is FPGA-mappable. In the case that the given probabilities cannot be duplicated but can be freely chosen, we provide an optimal choice. In the case that the given probabilities can be duplicated and can be freely chosen, we demonstrate how to generate arbitrary decimal probabilities from small sets -- a single probability or a pair of probabilities -- through combinational logic.