Abstract: Automated circuit design is provided. The method comprises mapping a number of inputs to a number of desired outputs and receiving a number of specified constraints. A number of component specifications are determined for circuit components necessary to achieve the desired outputs from the inputs within the specified constraints. A number of circuit components are chosen based on the component specifications to optimize achievement of the desired outputs from the inputs within the specified constraints, the chosen circuit components are arranged in a number of circuits based the component specifications to optimize achievement of the desired outputs.

Abstract: A method for probabilistic computing is provided. The method comprises specifying a target distribution for a computational model, wherein the target distribution is defined by a function. A number of coin flips are performed with a number of weighted coinflip devices, wherein weights for the coinflip devices are determined by the function. The number of coinflips are then converted to a random number from the target distribution according to outputs of the weighted coinflip devices, wherein a circuit uses the coin flips as inputs to randomly activate bits in a binary representation of the random number.

Abstract: Sampling an artificial neural network is provided. The method comprises creating a number of sample matrices based on a weight matrix of a trained artificial neural network, wherein each element in the sample matrices is equal to one of a pair of numbers generated by stochastic neuromorphic hardware according to weights from the weight matrix corresponding to the elements in the sample matrices. A number of inferences are performed with the trained neural network, wherein the weight matrix of the trained neural network is replaced with the sample matrices, and wherein each inference is performed with a different one of the sample matrices. A confidence level of the inferences is determined according to deviations between the first choice and other choices made by the trained neural network across the inferences.

Abstract: A spiking retina microscope comprising microscope optics and a neuromorphic imaging sensor. The microscope optics are configured to direct a magnified image of a specimen onto the neuromorphic imaging sensor. The neuromorphic imaging sensor comprises a plurality of sensor elements that are configured to generate spike signals in response to integrated light from the magnified image reaching a threshold. The spike signals may be processed by a processor unit to generate a result, such as tracking biological particles in a specimen comprising biological material.

Abstract: A method for simulating a random walk using spiking neuromorphic hardware is provided. The method comprises receiving, by a buffer count neuron, spiking inputs from upstream mesh nodes, wherein the inputs include information packets comprising information associated with a simulation of a random walk process. A buffer generator neuron generates spikes until the buffer count reaches a first predefined threshold, after which it sends buffer spiking outputs to a spike count neuron. The spike count neuron counts the buffer spiking outputs, and a spike generator neuron generates spikes until the spike count neuron reaches a second specified threshold. The spike generator neuron then sends counter spiking outputs to a probability neuron, which selects downstream mesh nodes to receive the counter spiking outputs, wherein the spiking outputs include updated information packets. The probability neuron then sends the spiking outputs to the selected downstream nodes.

Abstract: A spiking retina microscope comprising microscope optics and a neuromorphic imaging sensor. The microscope optics are configured to direct a magnified image of a specimen onto the neuromorphic imaging sensor. The neuromorphic imaging sensor comprises a plurality of sensor elements that are configured to generate spike signals in response to integrated light from the magnified image reaching a threshold. The spike signals may be processed by a processor unit to generate a result, such as tracking biological particles in a specimen comprising biological material.