Probabilistic computing is an emerging computational paradigm that uses probabilistic circuits to efficiently solve optimization problems such as invertible logic, where traditional digital computations are difficult to solve. This paper proposes a true random number generator (TRNG) based on resistive random-access memory (RRAM), which is combined with an activation function implemented by a piecewise linear function to form a standard p-bit cell, one of the most important parts of a p-circuit. A p-bit multiplexing strategy is also applied to reduce the number of p-bits and improve resource utilization. To verify the superiority of the proposed probabilistic circuit, we implement the invertible p-circuit on a field-programmable gate array (FPGA), including AND gates, full adders, multi-bit adders, and multipliers. The results of the FPGA implementation show that our approach can significantly save the consumption of hardware resources.
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http://dx.doi.org/10.3390/mi13060924 | DOI Listing |
Hum Brain Mapp
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Center for MR Research, University Children's Hospital Zurich, Zurich, Switzerland.
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Department of Mathematics, Aston University, Birmingham B4 7ET, United Kingdom.
Understanding the relation between cortical neuronal network structure and neuronal activity is a fundamental unresolved question in neuroscience, with implications to our understanding of the mechanism by which neuronal networks evolve over time, spontaneously or under stimulation. It requires a method for inferring the structure and composition of a network from neuronal activities. Tracking the evolution of networks and their changing functionality will provide invaluable insight into the occurrence of plasticity and the underlying learning process.
View Article and Find Full Text PDFParkinsonism Relat Disord
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The Nash Family Center for Advanced Circuit Therapeutics at the Icahn School of Medicine at Mount Sinai West, New York, NY, 10019, United States.
Introduction: Subthalamic nucleus deep brain stimulation (STN DBS) improves motor symptoms of Parkinson's disease (PD), but its effect on motivation is controversial. Apathy, the lack of motivation, commonly occurs in PD and is often exacerbated after surgery and its concomitant levodopa reduction. Apathy and reward processing are associated with the ventromedial prefrontal cortex (vmPFC), which standard targeting strategies avoid by targeting the dorsolateral STN.
View Article and Find Full Text PDFNat Comput Sci
December 2024
School of Integrated Circuits, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China.
Labeling data is a time-consuming, labor-intensive and costly procedure for many artificial intelligence tasks. Deep Bayesian active learning (DBAL) boosts labeling efficiency exponentially, substantially reducing costs. However, DBAL demands high-bandwidth data transfer and probabilistic computing, posing great challenges for conventional deterministic hardware.
View Article and Find Full Text PDFJ Chem Theory Comput
December 2024
Department of Computer Science, University of California, Davis, California 95616, United States.
Many quantum algorithms rely on a quality initial state for optimal performance. Preparing an initial state for specific applications can considerably reduce the cost of probabilistic algorithms such as the well studied quantum phase estimation (QPE). Fortunately, in the application space of quantum chemistry, generating approximate wave functions for molecular systems is well studied, and quantum computing algorithms stand to benefit from importing these classical methods directly into a quantum circuit.
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