Abstract:
Techniques are described herein for classifying multiple device states using separate Bayesian classifiers. An example of a method described herein includes accessing sensor information of a device, wherein at least some of the sensor information is used in a first feature set and at least some of the sensor information is used in a second feature set; processing the first feature set using a first classification algorithm configured to determine a first proposed state of a first state type and a first proposed state of a second state type; processing the second feature set using a second classification algorithm configured to determine a second proposed state of the first state type and a second proposed state of the second state type; and determining a proposed state of the device as the first proposed state of the first state type and the second proposed state of the second state type.
Abstract:
A method for improving neural dynamics includes obtaining prototypical neuron dynamics. The method also includes modifying parameters of a neuron model so that the neuron model matches the prototypical neuron dynamics. The neuron dynamics comprise membrane voltages and/or spike timing.
Abstract:
resumo “método e aparelho para classificar múltiplos estados de dispositivo” trata-se de técnicas descritas no presente documento para classificar múltiplos estados de dispositivo com o uso de classificadores bayesianos separados. um exemplo de um método descrito no presente documento inclui acessar informações de sensor de um dispositivo, em que pelo menos algumas das informações de sensor são usadas em um primeiro conjunto de recursos e pelo menos algumas das informações de sensor são usadas em um segundo conjunto de recursos; processar o primeiro conjunto de recursos com o uso de um primeiro algoritmo de classificação configurado para determinar um primeiro estado proposto de um primeiro tipo de estado e um primeiro estado proposto de um segundo tipo de estado; processar o segundo conjunto de recursos com o uso de um segundo algoritmo de classificação configurado para determinar um segundo estado proposto do primeiro tipo de estado e um segundo estado proposto do segundo tipo de estado; e determinar um estado proposto do dispositivo como o primeiro estado proposto do primeiro tipo de estado e o segundo estado proposto do segundo tipo de estado. 1/1
Abstract:
Certain aspects of the present disclosure support efficient implementation of common neuron models. In an aspect, a first memory layout can be allocated for parameters and state variables of instances of a first neuron model, and a second memory layout different from the first memory layout can be allocated for parameters and state variables of instances of a second neuron model having a different complexity than the first neuron model.
Abstract:
A method for dynamically modifying synaptic delays in a neural network includes initializing a delay parameter and operating the neural network. The method further includes dynamically updating the delay parameter based on a program which is based on a statement including the delay parameter.
Abstract:
Techniques are described herein for classifying multiple device states using separate Bayesian classifiers. An example of a method described herein includes accessing sensor information of a device, wherein at least some of the sensor information is used in a first feature set and at least some of the sensor information is used in a second feature set; processing the first feature set using a first classification algorithm configured to determine a first proposed state of a first state type and a first proposed state of a second state type; processing the second feature set using a second classification algorithm configured to determine a second proposed state of the first state type and a second proposed state of the second state type; and determining a proposed state of the device as the first proposed state of the first state type and the second proposed state of the second state type.
Abstract:
A method of distributed computation includes computing a first set of results in a first computational chain with a first population of processing nodes and passing the first set of results to a second population of processing nodes. The method also includes entering a first rest state with the first population of processing nodes after passing the first set of results and computing a second set of results in the first computational chain with the second population of processing nodes based on the first set of results. The method further includes passing the second set of results to the first population of processing nodes, entering a second rest state with the second population of processing nodes after passing the second set of results and orchestrating the first computational chain.
Abstract:
A method of reducing computational complexity for a fixed point neural network operating in a system having a limited bit width in a multiplier-accumulator (MAC) includes reducing a number of bit shift operations when computing activations in the fixed point neural network. The method also includes balancing an amount of quantization error and an overflow error when computing activations in the fixed point neural network.
Abstract:
A method of detecting unknown classes is presented and includes generating a first classifier for multiple first classes. In one configuration, an output of the first classifier has a dimension of at least two. The method also includes designing a second classifier to receive the output of the first classifier to decide whether input data belongs to the multiple first classes or at least one second class.
Abstract:
A method for improving neural dynamics includes obtaining prototypical neuron dynamics. The method also includes modifying parameters of a neuron model so that the neuron model matches the prototypical neuron dynamics. The neuron dynamics comprise membrane voltages and/or spike timing.