8660results about "Physical realisation" patented technology

The invention provides an apparatus and method for realizing an accelerator of a sparse convolutional neural network. According to the invention, the apparatus herein includes a convolutional and pooling unit, a full connection unit and a control unit. The method includes the following steps: on the basis of control information, reading convolutional parameter information, and input data and intermediate computing data, and reading full connected layer weight matrix position information, in accordance with the convolutional parameter information, conducting convolution and pooling on the input data with first iteration times, then on the basis of the full connected layer weight matrix position information, conducting full connection computing with second iteration times. Each input data is divided into a plurality of sub-blocks, and the convolutional and pooling unit and the full connection unit separately operate on the plurality of sub-blocks in parallel. According to the invention, the apparatus herein uses a specific circuit, supports a full connected layer sparse convolutional neural network, uses parallel ping-pang buffer design and assembly line design, effectively balances I / O broadband and computing efficiency, and acquires better performance power consumption ratio.

A physical neural network is disclosed, which includes a connection network comprising a plurality of molecular conducting connections suspended within a connection gap formed between one or more input electrodes and one or more output electrodes. One or more molecular connections of the molecular conducting connections can be strengthened or weakened according to an application of an electric field across said connection gap. Thus, a plurality of physical neurons can be formed from said molecular conducting connections of said connection network. Additionally, a gate can be located adjacent said connection gap and which comes into contact with said connection network. The gate can be connected to logic circuitry which can activate or deactivate individual physical neurons among said plurality of physical neurons.

Systems, methods and devices for restoring or enhancing one or more motor functions of a patient are disclosed. The system comprises a biological interface apparatus and a joint movement device such as an exoskeleton device or FES device. The biological interface apparatus includes a sensor that detects the multicellular signals and a processing unit for producing a control signal based on the multicellular signals. Data from the joint movement device is transmitted to the processing unit for determining a value of a configuration parameter of the system. Also disclosed is a joint movement device including a flexible structure for applying force to one or more patient joints, and controlled cables that produce the forces required.

A system and method for processing machine learning techniques (such as neural networks) and other non-graphics applications using a graphics processing unit (GPU) to accelerate and optimize the processing. The system and method transfers an architecture that can be used for a wide variety of machine learning techniques from the CPU to the GPU. The transfer of processing to the GPU is accomplished using several novel techniques that overcome the limitations and work well within the framework of the GPU architecture. With these limitations overcome, machine learning techniques are particularly well suited for processing on the GPU because the GPU is typically much more powerful than the typical CPU. Moreover, similar to graphics processing, processing of machine learning techniques involves problems with solving non-trivial solutions and large amounts of data.

A pattern detecting apparatus has a plurality of hierarchized neuron elements to detect a predetermined pattern included in input patterns. Pulse signals output from the plurality of neuron elements are given specific delays by synapse circuits associated with the individual elements. This makes it possible to transmit the pulse signals to the neuron elements of the succeeding layer through a common bus line so that they can be identified on a time base. The neuron elements of the succeeding layer output the pulse signals at output levels based on a arrival time pattern of the plurality of pulse signals received from the plurality of neuron elements of the preceding layer within a predetermined time window. Thus, the reliability of pattern detection can be improved, and the number of wires interconnecting the elements can be reduced by the use of the common bus line, leading to a small scale of circuit and reduced power consumption.

Physical neural network systems and methods are disclosed. A physical neural network can be configured utilizing molecular technology, wherein said physical neural network comprises a plurality of molecular conductors, which form neural network connections thereof. A training mechanism can be provided for training said physical neural network to accomplish a particular neural network task based on a neural network training rule. The neural network connections are formed between pre-synaptic and post-synaptic components of said physical neural network. The neural network generally includes dynamic and modifiable connections for adaptive signal processing. The neural network training mechanism can be based, for example, on the Anti-Hebbian and Hebbian (AHAH) rule and / or other plasticity rules.