Abstract: The invention provides methods for computing with chemicals by encoding digital data into a plurality of chemicals to obtain a dataset; translating the dataset into a chemical form; reading the data set; querying the dataset by performing an operation to obtain a perceptron; and analyzing the perceptron for identifying chemical structure and/or concentration of at least one of the chemicals, thereby developing a chemical computational language. The invention demonstrates a workflow for representing abstract data in synthetic metabolomes. Also presented are several demonstrations of kilobyte-scale image data sets stored in synthetic metabolomes, recovered at >99% accuracy.

Abstract: A hardware neural network system includes an input buffer for input neurons (Nbin), an output buffer for output neurons (Nbout), and a third buffer for synaptic weights (SB) connected to a Neural Functional Unit (NFU) and a control logic (CP) for performing synapses and neurons computations. The NFU pipelines a computation into stages, the stages including weight blocks (WB), an adder tree, and a non-linearity function.

Abstract: The invention provides methods for computing with chemicals by encoding digital data into a plurality of chemicals to obtain a dataset; translating the dataset into a chemical form; reading the data set; querying the dataset by performing an operation to obtain a perceptron; and analyzing the perceptron for identifying chemical structure and/or concentration of at least one of the chemicals, thereby developing a chemical computational language. The invention demonstrates a workflow for representing abstract data in synthetic metabolomes. Also presented are several demonstrations of kilobyte-scale image data sets stored in synthetic metabolomes, recovered at >99% accuracy.

Abstract: A hardware neural network system includes an input buffer for input neurons (Nbin), an output buffer for output neurons (Nbout), and a third buffer for synaptic weights (SB) connected to a Neural Functional Unit (NFU) and a control logic (CP) for performing synapses and neurons computations. The NFU pipelines a computation into stages, the stages including weight blocks (WB), an adder tree, and a non-linearity function.

Abstract: The invention provides methods for computing with chemicals by encoding digital data into a plurality of chemicals to obtain a dataset; translating the dataset into a chemical form; reading the data set; querying the dataset by performing an operation to obtain a perceptron; and analyzing the perceptron for identifying chemical structure and/or concentration of at least one of the chemicals, thereby developing a chemical computational language. The invention demonstrates a workflow for representing abstract data in synthetic metabolomes. Also presented are several demonstrations of kilobyte-scale image data sets stored in synthetic metabolomes, recovered at >99% accuracy.

Abstract: A method of accelerated iris recognition includes acquiring an image comprising at least an iris and a pupil, segmenting the iris and the pupil using a fully convolutional network (FCN) model, normalizing the segmented iris, encoding the normalized iris, the normalizing and encoding using a rubber sheet model and 1-D log Gabor filter, and masking the encoded iris.

Abstract: The invention provides methods for computing with chemicals by encoding digital data into a plurality of chemicals to obtain a dataset; translating the dataset into a chemical form; reading the data set; querying the dataset by performing an operation to obtain a perceptron; and analyzing the perceptron for identifying chemical structure and/or concentration of at least one of the chemicals, thereby developing a chemical computational language. The invention demonstrates a workflow for representing abstract data in synthetic metabolomes. Also presented are several demonstrations of kilobyte-scale image data sets stored in synthetic metabolomes, recovered at >99% accuracy.

Abstract: The invention provides methods for computing with chemicals by encoding digital data into a plurality of chemicals to obtain a dataset; translating the dataset into a chemical form; reading the data set; querying the dataset by performing an operation to obtain a perceptron; and analyzing the perceptron for identifying chemical structure and/or concentration of at least one of the chemicals, thereby developing a chemical computational language. The invention demonstrates a workflow for representing abstract data in synthetic metabolomes. Also presented are several demonstrations of kilobyte-scale image data sets stored in synthetic metabolomes, recovered at >99% accuracy.

Abstract: A method of accelerated iris recognition includes acquiring an image comprising at least an iris and a pupil, segmenting the iris and the pupil using a fully convolutional network (FCN) model, normalizing the segmented iris, encoding the normalized iris, the normalizing and encoding using a rubber sheet model and 1-D log Gabor filter, and masking the encoded iris.

Abstract: This invention relates to a power mapping and modeling system for integrated circuits.

Abstract: This invention relates in general to a system, apparatus, and method comprises one or more mapping and modeling systems used for power estimation, management, and improved efficiencies for the integrated circuit.

Abstract: A placement technique for designing a layout of an integrated circuit by calculating clustering scores for different pairs of objects in the layout based on connections of two objects in a given pair and the sizes of the two objects, then grouping at least one of the pairs of objects into a cluster based on the clustering scores, partitioning the objects as clustered and ungrouping the cluster after partitioning. The pair of objects having the highest clustering score are grouped into the cluster, and the clustering score is directly proportional to the total weight of connections between the two objects in the respective pair. The clustering scores are preferably inserted in a binary heap to identify the highest clustering score. After grouping, the clustering score for any neighboring object of a clustered object is marked to indicate that the clustering score is invalid and must be recalculated. The calculating and grouping are then repeated iteratively based on the previous clustered layout.