National Yang Ming Chiao Tung University

Taiwan Semiconductor Manufacturing Comp. Ltd.

Advantech Co., Ltd.

Chih-Cheng Chang

Tuo-Hung Hou

Jen-Chieh Liu

Feng-Hsiang Chung

Yi-Bin Yu

A task dispatching method for EPD tag devices includes receiving a task command; the server system forwarding the task command to a target router to which the target EPD tag device is connected; the target router checking whether the target EPD tag device is currently connected thereto; when it is determined that the target EPD tag device is not currently connected to the target router, the target router notifying the server system that the target EPD tag device is offline; in response to receiving an online notification indicating that the target EPD tag device is connected to another router, the server system updating a connection list accordingly, and forwarding the task command to an updated target router; and when it is determined that the target EPD tag device is currently connected to the target router, the target router delivering the task command to the target EPD tag device.

Task dispatching method and system for electrophoretic display (EPD) tag devices

A device includes first wires, second wires, resistors, and a processor. Input signals are transmitted from the first wires through the resistors to the second wires. The processor receives a sum value of the input signals from one of the second wires, and shifts the sum value by a nonlinear activation function to generate a shifted sum value. The processor calculates a backpropagation value based on the shifted sum value and a target value, and generates a pulse number based on a corresponding input signal of the input signal and the backpropagation value. Each of a value of the corresponding input signal and the backpropagation value is higher than or equal to a threshold value. The processor applies a voltage pulse to one of the resistors related to the corresponding input signal based on the pulse number.

Device and method for operating the same

A neural network processing system includes at least one synapse and a neuron circuit. The synapse receives an input signal and has an external weighted value and an internal weighted value, and the internal weighted value has a variation caused by an external stimulus. When the variation of the internal weighted value accumulates to a threshold value, the external weighted value varies and the input signal is multiplied by the external weighted value of the synapse to generate a weighted signal. A neuron circuit is connected with the synapse to receive the weighted signal transmitted by the synapse, and calculates and outputs the weighted signal. The present invention can simultaneously accelerate the prediction and learning functions of the deep learning and realize a hardware neural network with high precision and real-time learning.

Growing community of inventors

Taipei, Taiwan

Chih-Cheng Chang