EFFICIENT AND SCALABLE SYSTEMS FOR CALCULATING NEURAL NETWORK CONNECTIVITY IN AN EVENT-DRIVEN WAY
First Claim
1. A scalable system for recalculating, in an event-driven manner, property parameters including connectivity parameters of a neural network, the system comprises:
- an input component that receives a time varying input signal;
a storage component for storing the property parameters of the neural network;
a state machine capable of recalculating property parameters of the neural network, wherein the property parameters include connectivity among neurons of the neural network; and
an output component that generates output signals reflective of the calculated property parameters of the neural network and the input signal.
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Abstract
Systems and methods achieving scalable and efficient connectivity in neural algorithms by re-calculating network connectivity in an event-driven way are disclosed. The disclosed solution eliminates the storing of a massive amount of data relating to connectivity used in traditional methods. In one embodiment, a deterministic LFSR is used to quickly, efficiently, and cheaply re-calculate these connections on the fly. An alternative embodiment caches some or all of the LFSR seed values in memory to avoid sequencing the LFSR through all states needed to compute targets for a particular active neuron. Additionally, connections may be calculated in a way that generates neural networks with connections that are uniformly or normally (Gaussian) distributed.
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Citations
20 Claims
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1. A scalable system for recalculating, in an event-driven manner, property parameters including connectivity parameters of a neural network, the system comprises:
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an input component that receives a time varying input signal; a storage component for storing the property parameters of the neural network; a state machine capable of recalculating property parameters of the neural network, wherein the property parameters include connectivity among neurons of the neural network; and an output component that generates output signals reflective of the calculated property parameters of the neural network and the input signal. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer-implemented method for recalculating network property parameters of a neural network including connectivity parameters in an event-driven manner, the method comprises:
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initializing property parameters of the neural network; receiving, at an evaluating neuron of the neural network, a neural input corresponding to a time varying input signal to the neural network; recalculating by a state machine of the neural network at least some of the property parameters of the evaluating neuron, wherein the property parameters are random but determined after initialization; determining whether the evaluating neuron is to generate a neural output to its target neurons in the neural network; and if the evaluating neuron is determined to generate a neural output to its target neurons in the neural network, propagating the output of the evaluating neuron to its target neurons. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification