Abstract: In one aspect, A method for computing neural network computation includes the step of, providing plurality of neurons, coupled with a plurality of inputs, through a plurality of synapses. Each neuron output is given by an equation ?(Xi*Yi)+b. Xi*Yi comprises the ith synapse of the neuron. Xi comprises a set of Xi input vectors. Each Xi input vector is translated into an equivalent electrical signal for an ith corresponding synapse of the plurality of neurons, Yi comprises a set of Yi weight vectors, wherein each Yi weight vector comprises a parameter for the ith corresponding synapse of the plurality of neurons. Each synapse is a sub-system and the sub-system comprises a negative vector neural circuit, a positive vector neural circuit, and a set of four non-volatile memory weight cells for computation. The method includes the step of identifying the input vector x as a positive input vector or a negative input vector.

Abstract: In one aspect, A method for computing neural network computation includes the step of, providing plurality of neurons, coupled with a plurality of inputs, through a plurality of synapses. Each neuron output is given by an equation ?(Xi*Yi)+b. Xi*Yi comprises the ith synapse of the neuron. Xi comprises a set of Xi input vectors. Each Xi input vector is translated into an equivalent electrical signal for an ith corresponding synapse of the plurality of neurons, Yi comprises a set of Yi weight vectors, wherein each Yi weight vector comprises a parameter for the ith corresponding synapse of the plurality of neurons. Each synapse is a sub-system and the sub-system comprises a negative vector neural circuit, a positive vector neural circuit, and a set of four non-volatile memory weight cells for computation. The method includes the step of identifying the input vector x as a positive input vector or a negative input vector.