Industrial Technology Research Institute

Yao-Hua Chen

Juin-Ming Lu

Chih-Tsun Huang

Jing-Jia Liou

Hsun-Lun Huang

Chun-Chen Chen

Che-Wei Hsu

Yeong-Jar Chang

Ming-Der Shieh

Chun-Hung Lai

Jai-Ming Lin

Wan-Shan Hsieh

Yen-Fu Chang

Yu-Xiang Yen

Ya-Ting Shyu

Chun Wei Chen

Wei-Shiang Lin

Mei-Ling Chi

A processing element architecture adapted to a convolution comprises a plurality of processing elements and a delayed queue circuit. The plurality of processing elements includes a first processing element and a second processing element, wherein the first processing element and the second processing element perform the convolution according to a shared datum at least. The delayed queue circuit connects to the first processing element and connects to the second processing element. The delayed queue circuit receives the shared datum sent by the first processing element, and sends the shared datum to the second processing element after receiving the shared datum and waiting for a time interval.

Processing elements array that includes delay queues between processing elements to hold shared data

An adaptive learning power modeling method includes: sampling at least one of a plurality of network components to form a power consumption evaluation network according to at least one parameter within a parameter range; evaluating a predictive power consumption of a to-be-measured circuit by the power consumption evaluation network; training and evaluating an actual power consumption and the predictive power consumption of the to-be-measured circuit by the power consumption evaluation network to obtain an evaluation result; and performing training according to the evaluation result to determine whether to change the power consumption evaluation network.

Hardware structure aware adaptive learning based power modeling method and system

A DNN hardware accelerator and an operation method of the DNN hardware accelerator are provided. The DNN hardware accelerator includes: a network distributor for receiving an input data and distributing respective bandwidth of a plurality of data types of a target data amount based on a plurality of bandwidth ratios of the target data amount; and a processing element array coupled to the network distributor, for communicating data of the data types of the target data amount between the network distributor based on the distributed bandwidth of the data types.

Deep neural networks (DNN) hardware accelerator and operation method thereof

Disclosed are a timing estimation method and a simulator. The method is applied to a function verification model. In the method, the model issues a first access issue at a first time point; receives a first response to the first access issue from the bus at a second time point; calculates a delay time between the first and second time points; determines whether the delay time is longer than or substantially equal to a transmission time corresponding to the first access issue; issues a second access issue if yes; and issues the second access issue in a compensation time counting from the second time point if not. The compensation time is not longer than the difference between the transmission time and the delay time.

Timing esimation method and simulator

An embodiment of a thermal estimation device including a temperature model generator, a temperature gradient calculator, and a thermal sensing analyzer is disclosed. The temperature model generator generates a temperature model based on an initial power consumption, an initial area and an initial coordination of a circuit module. The temperature gradient calculator substitutes at least one of a testing area, a testing power or a testing coordinate of the circuit module into the temperature model for correspondingly estimating an temperature estimation function. The thermal sensing analyzer differentiates the temperature estimation function. When an absolute value of a differential result of the temperature estimation function resulted from a constant is closest to zero or is zero, outputting the constant as an optimized parameter.

Thermal estimation device and thermal estimation method

A power consumption estimation method is applied to an image with N rows of pixels, and comprises a pixel estimation procedure comprising performing an estimation sub-procedure pixel by pixel for each of a plurality of pixels in one row of the N rows of pixels to obtain a plurality of pixel energy consumption values respectively corresponding to the plurality of pixels in said one row of the N rows, and obtaining a row power consumption value corresponding to said one row of the N rows according to the plurality of pixel energy consumption values. The estimation sub-procedure comprises obtaining pixel content information corresponding to one of the plurality of pixels, and determining the pixel energy consumption value according to the pixel content information. The pixel energy consumption value indicates pixel energy consumption generated by performing a predetermined image processing procedure for said one of the plurality of pixels.

Power consumption estimation method and power consumption estimation apparatus

A memory transaction-level modeling method and a memory transaction-level modeling system are provided. The memory transaction-level modeling method is used for simulating the operation of outputting at least one command to the memory. The memory includes a plurality of banks each of which corresponds with a bank status table. The memory transaction-level modeling method includes the following steps: An event is received. Whether one of the bank status tables is needed to be updated is determined. If one of the bank status tables is needed to be updated, this bank status table is recovered according to a TMP queue. A command is outputted to the memory according to a command queue. The outputted command is stored in the TMP queue. Some of the bank status tables are updated and others of the bank status tables are kept unchanged.

Growing community of inventors

Changhua County, Taiwan

Yao-Hua Chen