• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.1
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• pp.15-23
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• 2009

Microprocessors, which need correct arithmetic operations, have been the subject of in-depth research in relation to soft errors. Of the existing microprocessor devices, the memory cell is the most vulnerable to soft errors. Moreover, when soft errors emerge in a memory cell, the processes and operations are greatly affected because the memory cell contains important information and instructions about the entire process or operation. Users do not realize that if soft errors go undetected, arithmetic operations and processes will have unexpected outcomes. In the field of architectural design, the tool that is commonly used to detect and correct soft errors is the error check and correction code. The Itanium, IBM PowerPC G5 microprocessors contain Hamming and Rasio codes in their level-2 cache. This research, however, focuses on huge server devices and does not consider power consumption. As the operating and threshold voltage is currently shrinking with the emergence of high-density and low-power embedded microprocessors, there is an urgent need to develop ECC (error check correction) circuits. In this study, the in-output data of the level-2 cache were analyzed using SimpleScalar-ARM, and a 32-bit H-matrix for the level-2 cache of an embedded microprocessor is proposed. From the point of view of power consumption, the proposed H-matrix can be implemented using a schematic editor of Cadence. Therefore, it is comparable to the modified Hamming code, which uses H-spice. The MiBench program and TSMC 0.18 um were used in this study for verification purposes.

• Journal of Sensor Science and Technology
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• v.7 no.2
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• pp.124-130
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• 1998

High-speed 3D vision systems are essential for autonomous robot or vehicle control applications. In our study, a stereo vision process has been developed. It consists of three steps : extraction of edges in right and left images, matching corresponding edges and calculation of the 3D map. This process is implemented in a VME 150/40 Imaging Technology vision system. It is a modular system composed by a display, an acquisition, a four Mbytes image frame memory, and three computational cards. Programmable accelerator computational modules are running at 40 MHz and are based on TMS320C31 DSP with a $64{\times}32$ bit instruction cache and two $1024{\times}32$ bit internal RAMs. Each is equipped with 512 Kbytes static RAM, 4 Mbytes image memory, 1 Mbytes flash EEPROM and a serial port. Data transfers and communications between modules are provided by three 8 bit global video bus, and three local configurable pipeline 8 bit video bus. The VME bus is dedicated to system management. Tasks between DSPs are distributed as follows: two DSPs are used to edges detection, one for the right image and the other for the left one. The last processor computes the matching process and the 3D calculation. With $512{\times}512$ pixels images, this sensor generates dense 3D maps at a rate of about 1 Hz depending of the scene complexity. Results can surely be improved by using a special suited multiprocessors cards.