This invention presents an approach to solve complex computational problems, specifically focusing on the "Maximum Cut" problem. By leveraging a novel PrxCa1-xMnO3-based Stochastic Neuron for Boltzmann Machine (BM), the system introduces a new paradigm for tackling NP Hard problems, which have wide-ranging practical implications. The design incorporates analog and digital components to create a stochastic neuron capable of making probabilistic decisions, mimicking the behavior of neurons in the brain. By harnessing the stochastic switching properties of PCMO RRAM, the system achieves remarkable efficiency in solving NP Hard problems.
Figure (1) Block diagram of the Markov chain mathematical model for a neuron is divided into 3 subparts: Stochastic Set (red) models stochastic spiking; Down Counter (blue) models the refractory period; Condition Select (green) decides the output of the neuron depending on the current state of neuron; (2) The circuit level implementation consists of the stochastic function (SF) block in addition to the deterministic logic.
Imagine trying to solve a really tricky puzzle where every possible solution needs to be checked one by one. These puzzles are called NP Hard problems, and they pop up in many important areas like planning routes, organizing data, and securing information. The problem is, our regular computers find these puzzles super hard too because they have to check every option which takes up a lot of time and memory. So, there is a need to devise a method to solve NP hard problems more efficiently.
- Innovative Material Use: Employs PrxCa1-xMnO3 (PCMO) RRAM for stochastic neuron implementation.
- Analog Sigmoid Function: Utilizes PCMO's analog sigmoidal switching for probability-based spike generation.
- Integrated Synaptic and Neuronal Functionality: Combines analog synaptic and integrate-and-fire (IF) neuronal functions in PCMO RRAM.
- Intrinsic Stochasticity: Achieves stochastic behavior without external noise sources or magnetic fields.
- Superior Algorithm Performance: Outperforms heuristic algorithms like Goemans-Williamson in solving NP-hard problems.
The stochastic switching behavior of PCMO RRAM has been experimentally demonstrated.
Current implementations show promising energy benchmarks compared to traditional von-Neumann architectures. Challenges remain in scaling the technology for large-scale integration in neuromorphic computing systems.
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This innovation empowers everyday individuals with simplified tools for making informed decisions, enhancing problem-solving abilities. It also enhances everyday technology by contributing to the development of faster, more energy-efficient computing systems.
- Enhancing artificial intelligence algorithms for improved pattern recognition and optimization tasks
- Enabling energy-efficient and high-performance computing solutions for various industries such as healthcare, finance, and automotive
Geography of IP
Type of IP
201921037296
511679