Computing with dynamical systems in the post-CMOS era

A. Parihar, N. Shukla, S. Datta, A. Raychowdhury
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引用次数: 6

Abstract

In the pursuit for building hardware accelerators to compute optimization problems researchers realize that the challenges in achieving this objective lie not only in implementing the hardware but also in the formulating the computing fundamentals of such designs. Neural network algorithms are considered most suited for this task, as there is usually a direct description of distributed computing entities, called “neurons”, and their interactions which can be mapped to both electronic and non-electronic hardware. In this regard, coupled oscillator systems have been studied where individual oscillators correspond to neurons and the information is encoded in either phase or frequency. But as is the case with neural networks, the computational power of the network depends on complexity of interactions among oscillators, and it is a challenge to implement oscillator networks with complex simultaneous interactions among multiple oscillators. Sinusoidal oscillators with assumption of weak linear phase coupling, akin to Kumamoto models, have been studied in theory but implementing such oscillators with weak couplings and encoding information in phase or frequency have been a challenge. Examples of using novel devices for making neural network hardware include memristor based neuromorphic synapses [1] and spin-torque oscillator (STO) based systems [2]. In our work, we use relaxation oscillators coupled using passive elements - capacitances or resistances - without the assumption of weak linear phase couplings. Our theoretical models are derived from circuit implementations, instead of the other way round, which means there are only engineering challenges in implementing the hardware, and no modeling discrepancies. We have explored two kinds of implementations - (a) simple pairwise coupling scheme with information encoded as frequency for pattern matching and associative computing, and (b) complex global coupling with information encoded in phase for the NP-hard graph coloring problem. We have been demonstrated in theory, using simulations and experimental implementations using VO2 devices, the working of such coupled relaxation oscillator networks.
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后cmos时代的动态系统计算
在构建硬件加速器来计算优化问题的过程中,研究人员意识到实现这一目标的挑战不仅在于硬件的实现,还在于这种设计的计算基础的制定。神经网络算法被认为是最适合这项任务的,因为通常有分布式计算实体的直接描述,称为“神经元”,它们的相互作用可以映射到电子和非电子硬件。在这方面,已经研究了耦合振荡器系统,其中单个振荡器对应于神经元,并且信息以相位或频率编码。但与神经网络一样,网络的计算能力取决于振子之间相互作用的复杂性,实现具有多个振子之间复杂同时相互作用的振子网络是一项挑战。假设弱线性相位耦合的正弦振荡器,类似于熊本模型,已经在理论上进行了研究,但实现这种具有弱耦合和相位或频率编码信息的振荡器一直是一个挑战。使用新设备制造神经网络硬件的例子包括基于忆阻器的神经形态突触[1]和基于自旋-扭矩振荡器(STO)的系统[2]。在我们的工作中,我们使用使用无源元件(电容或电阻)耦合的弛豫振荡器,而不假设弱线性相位耦合。我们的理论模型来源于电路实现,而不是相反,这意味着在实现硬件时只有工程挑战,并且没有建模差异。我们已经探索了两种实现方案——(a)模式匹配和关联计算中以频率编码信息的简单成对耦合方案,以及(b) NP-hard图着色问题中以相位编码信息的复杂全局耦合方案。我们已经从理论上,通过模拟和使用VO2器件的实验实现证明了这种耦合弛豫振荡器网络的工作原理。
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