Tsinghua University

Lynxi Technologies Co., Ltd.

Luping Shi

Yaolong Zhu

Jing Pei

Yangshu Shen

Rong Zhao

Zhenzhi Wu

Guoqi Li

Zheyu Yang

Haizheng Xu

Wei He

Lei Deng

Shuang Wu

Guanrui Wang

Embodiments of the present disclosure provide a cone-rod dual-modality neuromorphic vision sensor, including: a first preset quantity of voltage-mode active pixel sensor (APS) circuits and a second preset quantity of current-mode APS circuits, where each of the voltage-mode APS circuits includes a first-type photosensitive device, and each of the current-mode APS circuits includes a second-type photosensitive device. The voltage-mode APS can output a target voltage signal representing light intensity information in a target light signal. The obtained target voltage signal represents the light intensity information with a higher precision, and therefore an image with higher quality can be obtained, that is, the image has a higher signal-noise ratio. The voltage-mode APS can output a specified digital signal representing light intensity gradient information in the target light signal, to ensure performance indicators such as an image dynamic range and a shooting speed of the neuromorphic vision sensor, thereby making the neuromorphic vision sensor more stable and robust.

Cone-rod dual-modality neuromorphic vision sensor

Embodiments of the present disclosure provide a dual-modality neuromorphic vision sensor. A first-type current-mode active pixel sensor (APS) circuit can mimic excitatory rod cells, to perceive light intensity gradient information in a target light signal, thereby improving a dynamic arrange of an image sensed by a neuromorphic, vision sensor and its shooting speed. In addition, a first-type control switch is introduced for each of non-target first-type photosensitive devices, to control the obtained light intensity gradient information, and adjust the dynamic arrange of the image sensed by the neuromorphic vision sensor, thereby adjusting the shooting speed, and realizing a reconfigurable effect A voltage-mode APS can mimic cone cells, to output a target voltage signal representing light intensity information in the target light signal, and perceive the light intensity information in the target light signal. In this way, the obtained light intensity information represented by the target voltage signal has a higher precision, thereby ensuring the image quality.

Dual-modality neuromorphic vision sensor

The present disclosure provides a data transmission method and device for a network on chip and an electronic apparatus. The method includes: receiving, by a second network node, a first data packet sent by a first network node, the first data packet including first identification information and a data packet payload; determining, by the second network node, valid transmission information and second identification information corresponding to the valid transmission information according to the first identification information; determining, by the second network node, a second data packet according to the second identification information and the data packet payload; and sending, by the second network node, the second data packet according to the valid transmission information.

Data transmission method and device for network on chip and electronic apparatus

There is provided a neural network information receiving method and system, and a sending method and system. The receiving method comprises: acquiring a reception initiation time for neuron information (S); receiving rostral neuron information output by rostral neurons (S); acquiring delay information of the rostral neuron information according to the reception initiation time, the rostral neuron information and a delay algorithm (S); and determining composite information output by the rostral neurons according to the rostral neuron information and the delay information (S). The receiving method and system and sending method and system take the delay information into consideration in the output information of neurons, such that the neuron information is able to carry more detailed time-domain information, thus augmenting operation modes of the neurons and enhancing the generalization ability of the whole network.

Neural network information receiving method, sending method, system, apparatus and readable storage medium

The disclosure relates to a self-adaptive leakage value neuron information processing method and system. The method includes: receiving front end pulse neuron output information; reading current pulse neuron information, wherein the current pulse neuron information includes self-adaptive membrane potential leakage information; calculating current pulse neuron output information according to the front end pulse neuron output information and the current pulse neuron information; updating the self-adaptive membrane potential leakage information according to the current pulse neuron output information; outputting the current pulse neuron output information. The self-adaptive leakage value neuron information processing system utilizes self-adaptive membrane potential leakage information to participate in calculation of current pulse neuron output information, and utilizes the calculated current pulse neuron output information to update the self-adaptive membrane potential information to participate in calculation of a next time step, and a good balance between sensitivity and stability of a neural network is achieved.

Self-adaptive threshold neuron information processing method, self-adaptive leakage value neuron information processing method, system computer device and readable storage medium

The present disclosure provides a brain-like computing chip and a computing device. The brain-like computing chip includes is a many-core system composed of one or more functional cores, and data transmission is performed between the functional cores by means of a network-on-chip. The functional core includes at least one neuron processor configured to compute various neuron models, and at least one coprocessor coupled to the neuron processor and configured to perform an integral operation and/or a multiply-add-type operation; and the neuron processor is capable of calling the coprocessor to perform the multiply-add-type operation.

Brain-like computing chip and computing device

Provided are a path creation method and device for a network on chip and electronic apparatus. The method includes: receiving, by a second network node, a first data packet sent by a first network node, the first data packet carrying first idle address identification information, a destination network node address and path creation identification information, the first idle address identification information indicating a first idle position in a first path routing table of the first network node, the path creation identification information indicating a creation of a path; storing, by the second network node, the first idle address identification information in a second idle position in its second path routing table, and determining second idle address identification information; determining, by the second network node, a second data packet according to the second idle address identification information; and sending, by the second network node, the second data packet.

Path creation method and device for network on chip and electronic apparatus

Embodiments of the present disclosure provide multitask parallel processing method and apparatus, a computer device and a storage medium. The method is applied to a neural network consisting of a plurality of nodes, the neural network including at least one closed-loop path, and the method includes: inputting a data sequence to be computed into the neural network in a form of data packets, each of the data packets including multiple pieces of data; and computing, by the nodes in the closed-loop path, all the data in a currently received data packet each time a computation flow is started.

Growing community of inventors

Beijing, China

Luping Shi