Intel Corporation

Arnab Paul

Narayan Srinivasa

Gautham N Chinya

Shihao Ji

Chit Kwan Lin

Systems, apparatuses and methods may provide for replacing floating point matrix multiplication operations with an approximation algorithm or computation in applications that involve sparse codes and neural networks. The system may replace floating point matrix multiplication operations in sparse code applications and neural network applications with an approximation computation that applies an equivalent number of addition and/or subtraction operations.

Multiplication-free approximation for neural networks and sparse coding

Techniques and mechanisms for providing a logical state machine with a spiking neural network which includes multiple sets of nodes. Each of the multiple sets of nodes is to implement a different respective state, and each of the multiple spike trains is provided to respective nodes of each of the multiple sets of nodes. A given state of the logical state machine is implemented by configuring respective activation modes of each node of the corresponding set of nodes. The activation mode of a given node enables that node to signal, responsive to its corresponding spike train, that a respective state transition of the logical state machine is to be performed. In another embodiment, the multiple spike trains each represent a different respective character in a system used by data evaluated with the spiking neural network.

Parsing regular expressions with spiking neural networks

Techniques and mechanisms for servicing a search query using a spiking neural network. In an embodiment, a spiking neural network receives an indication of a first context of the search query, wherein a set of nodes of the spiking neural network each correspond to a respective entry of a repository. One or more nodes of the set of nodes are each excited to provide a respective cyclical response based on the first context, wherein a first cyclical response is by a first node. Due at least in part to a coupling of the excited nodes, a perturbance signal, based on a second context of the search query, results in a change of the first resonance response relative to one or more other resonance responses. In another embodiment, data corresponding to the first node is selected, based on the change, as an at least partial result of the search query.

Context-based search using spike waves in spiking neural networks

Techniques and mechanisms for determining the value of a weight associated with a synapse of a spiking neural network. In an embodiment, a first spike train and a second spike train are output, respectively, by a first node and a second node of the spiking neural network, wherein the synapse is coupled between said nodes. The weight is applied to signaling communicated via the synapse. A value of the weight is updated based on a product of a first value and a second value, wherein the first value is based on a first rate of spiking by the first spike train, and the second value is based on a second rate of spiking by the second spike train. In another embodiment, the weight is updated based on a product of a derivative of the first rate of spiking and a derivative of the second rate of spiking.

Device, system and method for varying a synaptic weight with a phase differential of a spiking neural network

Techniques and mechanisms for performing a Bayesian inference with a spiking neural network. In an embodiment, a parent node of the spiking neural network receives a first bias signal which is periodic. The parent node communicates a likelihood signal to a child node, wherein the parent node and the child node correspond to a first condition and a second condition, respectively. Based on a phase change which is applied to the first bias signal, the likelihood signal indicates a probability of the first condition. The child node also receives a signal which indicates an instance of the second condition. Based on the indication and a second bias signal, the child node signals to the first node that an adjustment is to be made to the phase change applied to the first bias signal. After the adjustment, the likelihood signal indicates an updated probability of the first condition.

Method, device and system to generate a Bayesian inference with a spiking neural network

The present disclosure provides for generating a spiking neural network. Generating a spiking neural network can include determining that a first input fan-in from a plurality of input neurons to each of a plurality of output neurons is greater than a threshold, generating a plurality of intermediate neurons based on a determination that the first input fan-in is greater than the threshold, and coupling the plurality of intermediate neurons to the plurality of input neurons and the plurality of output neurons, wherein each of the plurality of intermediate neurons has a second input fan-in that is less than the first input fan-in and each of the plurality of output neurons has a third input fan-in that is less than the first input fan-in.

Handling signal saturation in spiking neural networks

A method for managing computing includes replicating a subset of a machine state of a first computing device onto a second computing device, wherein the subset of the machine state is required to execute machine code. Execution of the machine code is offloaded to the second computing device.

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Hillsboro, OR, United States of America

Arnab Paul