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尖峰时间依赖可塑性(STDP)可以改善神经形态超大规模集成电路中的工艺变化。

Spike timing dependent plasticity (STDP) can ameliorate process variations in neuromorphic VLSI.

作者信息

Cameron Katherine, Boonsobhak Vasin, Murray Alan, Renshaw David

机构信息

School of Engineering and Electronics, The University of Edinburgh, UK.

出版信息

IEEE Trans Neural Netw. 2005 Nov;16(6):1626-37. doi: 10.1109/TNN.2005.852238.

Abstract

A transient-detecting very large scale integration (VLSI) pixel is described, suitable for use in a visual-processing, depth-recovery algorithm based upon spike timing. A small array of pixels is coupled to an adaptive system, based upon spike timing dependent plasticity (STDP), that aims to reduce the effect of VLSI process variations on the algorithm's performance. Results from 0.35 microm CMOS temporal differentiating pixels and STDP circuits show that the system is capable of adapting to substantially reduce the effects of process variations without interrupting the algorithm's natural processes. The concept is generic to all spike timing driven processing algorithms in a VLSI.

摘要

描述了一种瞬态检测超大规模集成(VLSI)像素,适用于基于尖峰定时的视觉处理深度恢复算法。一小阵列像素耦合到基于尖峰定时依赖可塑性(STDP)的自适应系统,该系统旨在减少VLSI工艺变化对算法性能的影响。0.35微米CMOS时间微分像素和STDP电路的结果表明,该系统能够在不中断算法自然过程的情况下进行自适应,以大幅减少工艺变化的影响。该概念对于VLSI中所有尖峰定时驱动的处理算法都是通用的。

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