自适应DPE算法的忆阻器仿真器:比较研究

Hussein Assaf, Y. Savaria, M. Sawan
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引用次数: 2

摘要

向量矩阵乘法(VMM)是一种复杂的运算,需要大量的计算能力来完成一次迭代。电阻的计算;包括忆阻器,是一种加速VMM的解决方案,它将乘法过程优化为几个步骤,而不管矩阵的大小。本文提出了一种基于忆阻器的自适应点积引擎(ADPE)算法,以提高VMM中的电阻计算过程。该算法对一个忆阻器层栅阵列电路进行一次在线训练,初步结果误差为5%。然而,记忆电阻器需要新的制造技术,其中使用这些器件的系统的设计和验证过程仍然具有挑战性。比较了几种适合于ADPE的忆阻器仿真电路,并根据芯片尺寸、所使用的电路元件和工作频率对模型进行了比较。
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Memristor Emulators for an Adaptive DPE Algorithm: Comparative Study
Vector Matrix Multiplication (VMM) is a complex operation requiring large computational power to fulfill one iteration. Resistive computing; including memristors, is one solution to speed up VMM by optimizing the multiplication process into few steps despite the matrices’ sizes. In this paper, we propose an Adaptive Dot Product Engine (ADPE) algorithm based on memristors for enhancing the process of resistive computing in VMM. The algorithm showed 5% error on preliminary results with one on-line training step for one layered crossbar array circuit of memristors. However memristors require new fabrication technologies where the design and validation processes of systems using these devices remains challenging. A comparison of various available circuits emulating a memristor suitable for ADPE is presented and models were compared based on chip size, circuit elements used and operating frequency.
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