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577results about "Exclusive-OR circuits" patented technology

Self repairing neural network

Some embodiments of the invention provide an integrated circuit (IC) with a defect-tolerant neural network. The neural network has one or more redundant neurons in some embodiments. After the IC is manufactured, a defective neuron in the neural network can be detected through a test procedure and then replaced by a redundant neuron (i.e., the redundant neuron can be assigned the operation of the defective neuron). The routing fabric of the neural network can be reconfigured so that it re-routes signals around the discarded, defective neuron. In some embodiments, the reconfigured routing fabric does not provide any signal to or forward any signal from the discarded, defective neuron, and instead provides signals to and forwards signals from the redundant neuron that takes the defective neuron's position in the neural network. In some embodiments that implement a neural network by re-purposing (i.e., reconfiguring) one or more individual neurons to implement neurons of multiple stages of the neural network, the IC discards a defective neuron by removing it from the pool of neurons that it configures to perform the operation(s) of neurons in one or more stages of neurons, and assigning this defective neuron's configuration(s) (i.e., its machine-trained parameter set(s)) to a redundant neuron. In some of these embodiments, the IC would re-route around the defective neuron and route to the redundant neuron, by (1) supplying machine-trained parameters and input signals (e.g., previous stage neuron outputs) to the redundant neuron instead of supplying these parameters and signals to the defective neuron, and (2) storing the output(s) of the redundant neuron instead of storing the output(s) of the defective neuron.
Owner:XCELSIS CORP

Three dimensional chip structure implementing machine trained network

Some embodiments provide a three-dimensional (3D) circuit structure that has two or more vertically stacked bonded layers with a machine-trained network on at least one bonded layer. As described above, each bonded layer can be an IC die or an IC wafer in some embodiments with different embodiments encompassing different combinations of wafers and dies for the different bonded layers. The machine-trained network in some embodiments includes several stages of machine-trained processing nodes with routing fabric that supplies the outputs of earlier stage nodes to drive the inputs of later stage nodes. In some embodiments, the machine-trained network is a neural network and the processing nodes are neurons of the neural network. In some embodiments, one or more parameters associated with each processing node (e.g., each neuron) is defined through machine-trained processes that define the values of these parameters in order to allow the machine-trained network (e.g., neural network) to perform particular operations (e.g., face recognition, voice recognition, etc.). For example, in some embodiments, the machine-trained parameters are weight values that are used to aggregate (e.g., to sum) several output values of several earlier stage processing nodes to produce an input value for a later stage processing node.
Owner:XCELSIS CORP

Avoiding forbidden data patterns in coded audio data

Any of several information processing techniques may be used in various information storage and transmission applications to prevent the occurrence of certain "forbidden" bit patterns. According to an encoding technique, a reversible coding process is used to generate an encoded representation of an information stream that cannot contain any forbidden data patterns. This may be accomplished by partitioning the information stream into segments and encoding each segment according to a respective encoding key that is selected such that the results of the coding process cannot contain a forbidden data pattern. According to one substitution technique, all occurrences of forbidden data patterns are replaced with permissible data patterns that do not otherwise occur in the information stream. This may be accomplished by partitioning the information stream into segments, identifying an unused data pattern in a respective segment, and carrying out the replacement of all occurrences of the forbidden data pattern in that segment. According to another substitution technique, all occurrences of a forbidden data pattern are replaced by any permissible data pattern. This may be accomplished by partitioning the information stream into segments, identifying occurrences of the substitution data pattern and the forbidden data pattern in a respective segment, constructing a flag for each occurrence, and replacing all occurrences of the forbidden data pattern in that segment with the substitution data pattern. Decoding keys, substitution data patterns, substitution flags or any other information needed to recover the original information is assembled with the modified information in a form that does not equal the forbidden data pattern.
Owner:DOLBY LAB LICENSING CORP

Three dimensional circuit implementing machine trained network

Some embodiments provide a three-dimensional (3D) circuit structure that has two or more vertically stacked bonded layers with a machine-trained network on at least one bonded layer. As described above, each bonded layer can be an IC die or an IC wafer in some embodiments with different embodiments encompassing different combinations of wafers and dies for the different bonded layers. The machine-trained network in some embodiments includes several stages of machine-trained processing nodes with routing fabric that supplies the outputs of earlier stage nodes to drive the inputs of later stage nodes. In some embodiments, the machine-trained network is a neural network and the processing nodes are neurons of the neural network. In some embodiments, one or more parameters associated with each processing node (e.g., each neuron) is defined through machine-trained processes that define the values of these parameters in order to allow the machine-trained network (e.g., neural network) to perform particular operations (e.g., face recognition, voice recognition, etc.). For example, in some embodiments, the machine-trained parameters are weight values that are used to aggregate (e.g., to sum) several output values of several earlier stage processing nodes to produce an input value for a later stage processing node.
Owner:XCELSIS CORP
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