Novel Neuromorphic Signal Cross-Correlation Device

Abstract (Set) The Johns Hopkins University seeks a partner to commercialize a novel signal cross-correlation engine. Utilizing silicon neurons, the engine significantly reduces the computational resources and time required to extract motion or to separate sound sources. Cross-correlation is a useful function in many engineering applications, from wireless communication to object recognition. Yet it has remained a computationally intense process until now. Description (Set) • Signal cross-correlation is an important requirement in many electronics systems, from CDMA cellular telephone networks to the Global Positioning System (GPS). Current cross-correlation technology requires a significant amount of computational power and energy.
• The novel engine outperforms by relying on simple, low-power neuromorphic circuitry, with each circuit consisting of two spiking neurons.
• Each circuit consists of two spiking neurons. The neuron pair produces the cross-correlation function for pairs of input signals without explicitly performing a mathematical algorithm. The circuit uses noisy integrate-and-fire neurons to produce autocorrelation information.
• The simplicity of the circuit creates several advantages over current commercially available technologies:
1. Reduction of computational intensity 2. Very low power requirements 3. High degree of scalability for a variety of different applications Proposed Use (Set) GPS, Neuroprosthetics, Neurostimulation, Neurofeedback, Neurosurgical

Inventor(s): Etienne-Cummings, Ralph ,Folowosele, Fopefolu,Tapson, Jonathan,Vismer, Mark

Type of Offer: Licensing



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