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Real-time regularity detection for robot vision using a customized architectural approach

机译:使用定制的架构方法进行机器人视觉的实时规律性检测

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The medium-level part of a real-time robot vision system involves many subsystems which combine both intensive arithmetic computations and complex decision-making. Moreover, recursive loops typically limit the throughput. Therefore, it is believed that these subsystems can only be efficiently realized within the required throughput specs by developing a BLSI ASIC. This applies also for the regularity detection unit which is the main topic discussed. During the architecture design, several tasks have to be addressed. Starting from the original signal flow graph, the dedicated I/O blocks and the customized, distributed off- and on-chip storage units have been derived. A number of time-multiplexed application-specific data-paths have been defined, optimized towards communication with the memories and towards maximal hardware-sharing within the throughput spec. They exploit the concurrency inherent in the algorithm and are balanced in terms of pipeline sections to increase the clock period achievable. In order to solve the timing bottle-neck in the steering of this complex (partly programmable) data-path organization, a hierarchically partitioned controller is constructed.
机译:实时机器人视觉系统的中级部分涉及许多子系统,这些子系统将密集的算术计算与复杂的决策结合在一起。此外,递归循环通常会限制吞吐量。因此,相信通过开发BLSI ASIC,只能在所需的吞吐量规格内有效地实现这些子系统。这也适用于规律性检测单元,这是讨论的主要主题。在体系结构设计期间,必须解决几个任务。从原始信号流程图开始,已经获得了专用的I / O模块以及定制的分布式分布式片上和片上存储单元。已经定义了许多时分复用的特定于应用程序的数据路径,这些路径针对与内存的通信和吞吐量规格内的最大硬件共享进行了优化。它们利用算法固有的并发性,并在流水线部分方面保持平衡,以增加可实现的时钟周期。为了解决这种复杂的(部分可编程的)数据路径组织的时序瓶颈,构造了一个分层的控制器。

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