Ultra-wide instantaneous bandwidth complex neuromorphic adaptive core processor
Described is a system for Neuromorphic Adaptive Core (NeurACore) signal processor for ultra-wide instantaneous bandwidth denoising of a noisy signal. The NeurACore signal processor includes a digital signal pre-processing unit for performing cascaded decomposition of a wideband complex valued In-phase and Quadrature-phase (I/Q) input signal in real time. The wideband complex valued I/Q input signal is decomposed into I and Q sub-channels. The NeurACore signal processor further includes a NeurACore and local learning layers for performing high-dimensional projection of the wideband complex valued I/Q input signal into a high-dimensional state space; a global learning layer for performing a gradient descent online learning algorithm; and a neural combiner for combining outputs of the global learning layer to compute signal predictions corresponding to the wideband complex valued I/Q input signal.
1. A Neuromorphic Adaptive Core (NeurACore) signal processor for ultra-wide instantaneous bandwidth denoising of a noisy signal, comprising:
a digital signal pre-processing unit, the digital signal pre-processing unit being operable for performing cascaded decomposition of a wideband complex valued In-phase and Quadrature-phase (I/Q) input signal in real time,
wherein the wideband complex valued I/Q input signal is decomposed into I and Q sub-channels;
a NeurACore and local learning layers, the NeurACore and local learning layers operable for performing high-dimensional projection of the wideband complex valued I/Q input signal into a high-dimensional state space;
a global learning layer, the global learning layer operable for performing a gradient descent online learning algorithm; and
a neural combiner, the neural combiner operable for combining outputs of the global learning layer to compute signal predictions corresponding to the wideband complex valued I/Q input signal.
2. The NeurACore signal processor as set forth in claim 1 , wherein the cascaded decomposition is a multi-layered I/Q decomposition scheme, wherein for each layer, a sample rate of the layer is reduced by half compared to a preceding layer in the cascaded decomposition.
3. The NeurACore signal processor as set forth in claim 2 , wherein the cascaded decomposition is a three layer I/Q decomposition scheme, and wherein the gradient descent online learning algorithm is an eight-dimensional gradient descent online learning algorithm.
4. The NeurACore signal processor as set forth in claim 3 , wherein the gradient descent online learning algorithm uses eight-dimensional state variables and weight matrices by cross coupling the eight-dimensional state variables in weights update equations and output layer update equations.
5. The NeurACore signal processor as set forth in claim 4 , wherein the digital signal pre-processing is further operable for implementing blind source separation (BSS) and feature extraction algorithms with updates to interpret denoised eight-dimensional state variables.
6. The NeurACore signal processor as set forth in claim 1 , wherein the NeurACore comprises high-dimensional signal processing nodes with adaptable parameters.
7. A computer program product comprising a non-transitory computer-readable medium having computer-readable instructions stored thereon, wherein the computer-readable instructions are executable by a computer having one or more processors for causing the one or more processors to perform operations of:
performing cascaded decomposition of a wideband complex valued In-phase and Quadrature-phase (I/Q) input signal in real time into I and Q sub-channels;
performing high-dimensional projection of the wideband complex valued I/Q input signal into a high-dimensional state space;
performing a gradient descent online learning algorithm; and
combining outputs of the global learning layer to compute signal predictions corresponding to the wideband complex valued I/Q input signal.
8. The computer program product as set forth in claim 7 , wherein the cascaded decomposition is a multi-layered I/Q decomposition scheme, wherein for each layer, a sample rate of the layer is reduced by half compared to a preceding layer in the cascaded decomposition.
9. The computer program product as set forth in claim 8 , wherein the cascaded decomposition is a three layer I/Q decomposition scheme, and wherein the gradient descent online learning algorithm is an eight-dimensional gradient descent online learning algorithm.
10. The computer program product as set forth in claim 9 , wherein the gradient descent online learning algorithm uses eight-dimensional state variables and weight matrices by cross coupling the eight-dimensional state variables in weights update equations and output layer update equations.
11. The computer program product as set forth in claim 10 , wherein blind source separation (BSS) and feature extraction algorithms are implemented with updates to interpret denoised eight-dimensional state variables.
12. A computer implemented method for ultra-wide instantaneous bandwidth denoising of a noisy signal, the method comprising an act of:
causing one or more processers to execute instructions encoded on a non-transitory computer-readable medium, such that upon execution, the one or more processors perform operations of:
performing cascaded decomposition of a wideband complex valued In-phase and Quadrature-phase (I/Q) input signal in real time into I and Q sub-channels;
performing high-dimensional projection of the wideband complex valued I/Q input signal into a high-dimensional state space;
performing a gradient descent online learning algorithm; and
combining outputs of the global learning layer to compute signal predictions corresponding to the wideband complex valued I/Q input signal.
13. The method as set forth in claim 12 , wherein the cascaded decomposition is a multi-layered I/Q decomposition scheme, wherein for each layer, a sample rate of the layer is reduced by half compared to a preceding layer in the cascaded decomposition.
14. The method as set forth in claim 13 , wherein the cascaded decomposition is a three layer I/Q decomposition scheme, and wherein the gradient descent online learning algorithm is an eight-dimensional gradient descent online learning algorithm.
15. The method as set forth in claim 14 , wherein the gradient descent online learning algorithm uses eight-dimensional state variables and weight matrices by cross coupling the eight-dimensional state variables in weights update equations and output layer update equations.
16. The method as set forth in claim 15 , wherein the digital signal pre-processing is further operable for implementing blind source separation (BSS) and feature extraction algorithms with updates to interpret denoised eight-dimensional state variables.