Abstract: The present disclosure provides a quick operation device for a nonlinear function, and a method therefor. The device comprises: a domain conversion part for converting an input independent variable into a corresponding value in a table lookup range; a table lookup part for looking up a slope and an intercept of the corresponding piecewise linear fitting based on the input independent variable or an independent variable processed by the domain conversion part; and a linear fitting part for obtaining, a final result in a way of linear fitting based on the slope and the intercept obtained, by means of table lookup, by the table lookup part. The present disclosure solves the problems of slow operation speed, large area of the operation device, and high power consumption caused by the traditional method.

Abstract: The present disclosure discloses an adder device, a data accumulation method and a data processing device. The adder device comprises: a first adder module provided with an adder tree unit, composed of a multi-stage adder array, and a first control unit, wherein the adder tree unit accumulates data by means of step-by-step accumulation based on a control signal of the first control unit; a second adder module comprising a two-input addition/subtraction operation unit and a second control unit, and used for performing an addition or subtraction operation on input data; a shift operation module for performing a left shift operation on output data of the first adder module; an AND operation module for performing an AND operation on output data of the shift operation module and output data of the second adder module; and a controller module.

Abstract: The present disclosure provides a data accumulation device and method, and a digital signal processing device. The device comprises: an accumulation tree module for accumulating input data in the form of a binary tree structure and outputting accumulated result data; a register module including a plurality of groups of registers and used for registering intermediate data generated by the accumulation tree module during an accumulation process and the accumulated result data; and a control circuit for generating a data gating signal to control the accumulation tree module to filter the input data not required to be accumulated, and generating a flag signal to perform the following control: selecting a result obtained after adding one or more of intermediate data stored in the register to the accumulated result as output data, or directly selecting the accumulated result as output data. Thus, a plurality of groups of input data can be rapidly accumulated to a group of sums in a clock cycle.

Abstract: The present invention discloses an apparatus and a method for performing a variety of transcendental function operations. The apparatus comprises a pre-processing unit group, a core unit and a post-processing unit group, wherein the pre-processing unit group is configured to transform an externally input independent variable a into x, y coordinates, an angle z, and other information k, and determine an operation mode to be used by the core unit; the core unit is configured to perform trigonometric or hyperbolic transformation on the x, y coordinates and the angle z, obtain transformed x?, y? coordinates and angle z?, and output them to the post-processing unit group; and the post-processing unit group is configured to transform the x?, y? coordinates and the angle z? input by the core unit according to the other information k and a function f input by the pre-processing unit group to obtain an output result c.

Abstract: Aspects for backpropagation in a fully connect layer of a convolutional neural network are described herein. The aspects may include a direct memory access unit configured to receive input data and one or more first data gradients from a storage device. The aspects may further include a master computation module configured to transmit the input data and the one or more first data gradients to one or more slave computation modules. The slave computation modules are respectively configured to multiply one of the one or more first data gradients with the input data to generate a default weight gradient vector.

Abstract: A communication structure comprises: a central node that is a communication data center of a network-on-chip and used for broadcasting or multicasting communication data to a plurality of leaf nodes; a plurality of leaf nodes that are communication data nodes of the network-on-chip and used for transmitting the communication data to the central node; and forwarder modules for connecting the central node with the plurality of leaf nodes and forwarding the communication data, wherein the plurality of leaf nodes are divided into N groups, each group having the same number of leaf nodes, the central node is individually in communication connection with each group of leaf nodes by means of the forwarder modules, the communication structure is a fractal-tree structure, the communication structure constituted by each group of leaf nodes has self-similarity, and the forwarder modules comprises a central forwarder module, leaf forwarder modules, and intermediate forwarder modules.

Abstract: The present disclosure provides a quick operation device for a nonlinear function, and a method therefor. The device comprises: a domain conversion part for converting an input independent variable into a corresponding value in a table lookup range; a table lookup part for looking up a slope and an intercept of the corresponding piecewise linear fitting based on the input independent variable or an independent variable processed by the domain conversion part; and a linear fitting part for obtaining, a final result in a way of linear fitting based on the slope and the intercept obtained, by means of table lookup, by the table lookup part. The present disclosure solves the problems of slow operation speed, large area of the operation device, and high power consumption caused by the traditional method.

Abstract: The present disclosure provides a data accumulation device and method, and a digital signal processing device. The device comprises: an accumulation tree module for accumulating input data in the form of a binary tree structure and outputting accumulated result data; a register module including a plurality of groups of registers and used for registering intermediate data generated by the accumulation tree module during an accumulation process and the accumulated result data; and a control circuit for generating a data gating signal to control the accumulation tree module to filter the input data not required to be accumulated, and generating a flag signal to perform the following control: selecting a result obtained after adding one or more of intermediate data stored in the register to the accumulated result as output data, or directly selecting the accumulated result as output data. Thus, a plurality of groups of input data can be rapidly accumulated to a group of sums in a clock cycle.

Abstract: The present disclosure discloses an adder device, a data accumulation method and a data processing device. The adder device comprises: a first adder module provided with an adder tree unit, composed of a multi-stage adder array, and a first control unit, wherein the adder tree unit accumulates data by means of step-by-step accumulation based on a control signal of the first control unit; a second adder module comprising a two-input addition/subtraction operation unit and a second control unit, and used for performing an addition or subtraction operation on input data; a shift operation module for performing a left shift operation on output data of the first adder module; an AND operation module for performing an AND operation on output data of the shift operation module and output data of the second adder module; and a controller module.

Abstract: The present disclosure provides an operation apparatus and method for an acceleration chip for accelerating a deep neural network algorithm. The apparatus comprises: a vector addition processor module for performing addition or subtraction of a vector, and/or a vectorized operation of a pooling layer algorithm in a deep neural network algorithm; a vector function value arithmetic unit module for performing a vectorized operation of a non-linear evaluation in the deep neural network algorithm; and a vector multiplier-adder module for performing a multiply-add operation on the vector, wherein the three modules execute a programmable instruction, and interact with each other to calculate values of neurons and a network output result of a neural network, and a variation amount of a synaptic weight representing the interaction strength of the neurons on an input layer to the neurons on an output layer.

Abstract: Aspects for modifying data in a multi-layer neural network (MNN) acceleration processor for neural networks are described herein. As an example, the aspects may include receiving one or more groups of input data, a predetermined weight value array, and connection data. Further, the aspects may include modifying the weight values included in the predetermined weight value array and the one or more groups of input data based on the connection data. Further still, the aspects may include calculating one or more groups of output data based on the modified weight values and the modified input data.

Abstract: Aspects for modifying data in a multi-layer neural network (MNN) acceleration processor for neural networks are described herein. As an example, the aspects may include receiving one or more groups of input data and connection data. Further, the aspects may include modifying the one or more groups of input data based on the connection data. Further still, the aspects may include calculating one or more groups of output data based on the modified input data.

Abstract: Aspects for modifying data in a multi-layer neural network (MNN) acceleration processor for neural networks are described herein. As an example, the aspects may include receiving a predetermined weight value array and connection data. Further, the aspects may include modifying the weight values included in the predetermined weight value array based on the connection data. Further still, the aspects may include calculating one or more groups of output data based on the modified weight values.

Abstract: An abrasive jet cutting system may include a differential pressure measurement apparatus configured to measure a differential pressure between points in an abrasive supply system. The differential pressure may be used to determine one or more conditions of the jet and the abrasive delivery. The measured differential pressure may be used in a feedback control system, feed forward control system, and/or an alarm or safety system.

Abstract: An abrasive jet cutting system may include a differential pressure measurement apparatus configured to measure a differential pressure between points in an abrasive supply system. The differential pressure may be used to determine one or more conditions of the jet and the abrasive delivery. The measured differential pressure may be used in a feedback control system, feed forward control system, and/or an alarm or safety system.

Abstract: An abrasive jet cutting system may include a differential pressure measurement apparatus configured to measure a differential pressure between points in an abrasive supply system. The differential pressure may be used to determine one or more conditions of the jet and the abrasive delivery. The measured differential pressure may be used in a feedback control system, feed forward control system, and/or an alarm or safety system.

Abstract: An abrasive jet cutting system may include a differential pressure measurement apparatus configured to measure a differential pressure between points in an abrasive supply system. The differential pressure may be used to determine one or more conditions of the jet and the abrasive delivery. The measured differential pressure may be used in a feedback control system, feed forward control system, and/or an alarm or safety system.