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Digital associative memory for word-parrallel Manhattan-distance-based vector quantization

机译:基于词语曼哈顿距离的矢量量化的数字关联记忆

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Digital Word-parallel associative-memory architecture capable of Manhattan-distance-based vector quantization is reported, which applies frequency dividers and clock counting to realize nearest Manhattan-distance (MD) search. Experimental verification was done with a 65 nm CMOS design implementing 128 reference vectors, each having 16 components and 16 bit per component. For the fabricated test chips 926 ps minimum search time and 2.13 mW power dissipation are measured at 120MHz and Vdd = 1.2V. At lower supply voltage of Vdd = 0.9V and lower frequency of 20MHz the power-dissipation reduces to 130 μW. In comparison to previous digital architecture a factor 100 smaller power delay product (estimated factor 16 when scaled to 65 nm CMOS) is achieved.
机译:报告了能够进行曼哈顿距离的向量量化的数字词并行关联 - 内存架构,其应用频分隔器和时钟计数来实现最近的曼哈顿距离(MD)搜索。使用65nm CMOS设计实现的实验验证,实现128个参考矢量,每个参考矢量具有16个组件,每个组件16位。对于制造的测试芯片926,PS最小搜索时间和2.13 MW功耗在120MHz和VDD = 1.2V下测量。在VDD = 0.9V的较低电源电压下,较低频率为20MHz,功率耗散降低至130μW。与以前的数字体系结构相比,实现了100个较小的功率延迟产品(缩放到65nm CMOS时的估计因子16)。

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