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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Analog Programmable Distance Calculation Circuit for Winner Takes All Neural Network Realized in the CMOS Technology
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Analog Programmable Distance Calculation Circuit for Winner Takes All Neural Network Realized in the CMOS Technology

机译：获奖者的模拟可编程距离计算电路采用CMOS技术实现的所有神经网络

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摘要

This paper presents a programmable analog current-mode circuit used to calculate the distance between two vectors of currents, following two distance measures. The Euclidean (L2) distance is commonly used. However, in many situations, it can be replaced with the Manhattan (L1) one, which is computationally less intensive, whose realization comes with less power dissipation and lower hardware complexity. The presented circuit can be easily reprogrammed to operate with one of these distances. The circuit is one of the components of an analog winner takes all neural network (NN) implemented in the complementary metal–oxide–semiconductor 0.18- technology. The learning process of the realized NN has been successfully verified by the laboratory tests of the fabricated chip. The proposed distance calculation circuit (DCC) features a simple structure, which makes it suitable for networks with a relatively large number of neurons realized in hardware and operating in parallel. For example, the network with three inputs occupies a relatively small area of 3900 . When operating in the L2 mode, the circuit dissipates 85 of power from the 1.5 V voltage supply, at maximum data rate of 10 MHz. In the L1 mode, an average dissipated power is reduced to 55 from 1.2 V voltage supply, while data rate is 12 MHz in this case. The given data rates are provided for the worst case scenario, where input currents differ by 1%–2% only. In this case, the settling time of the comparators used in the DCC is quite long. However, that kind of situation is very rare in the overall learning process.
机译：本文提出了一种可编程模拟电流模式电路，该电路用于根据两个距离测量来计算两个电流矢量之间的距离。欧几里德（L2）距离是常用的。但是，在许多情况下，可以用计算量较少的曼哈顿（L1）代替它，其实现具有较少的功耗和较低的硬件复杂性。所提供的电路可以很容易地重新编程为以这些距离之一工作。该电路是模拟获胜者的组成部分之一，采用了在互补金属氧化物半导体0.18技术中实现的所有神经网络（NN）。所实现的神经网络的学习过程已通过所制造芯片的实验室测试成功验证。所提出的距离计算电路（DCC）具有简单的结构，使其适合于具有相对大量神经元的网络，这些神经元通过硬件实现并可以并行操作。例如，具有三个输入的网络占用3900的相对较小的区域。在L2模式下工作时，电路以最大10 MHz的数据速率从1.5 V电压电源消耗85功率。在L1模式下，平均耗散功率从1.2 V电压降至55，而此时的数据速率为12 MHz。给定的数据速率是在最坏的情况下提供的，其中输入电流仅相差1％–2％。在这种情况下，DCC中使用的比较器的建立时间非常长。但是，这种情况在整个学习过程中很少见。

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