Neuromorphic, Digital, and Quantum Computation With Memory Circuit Elements

Abstract

Abstract-Memory effects are ubiquitous in nature and the class of memory circuit elements -which includes memristive, memcapacitive and meminductive systems -shows great potential to understand and simulate the associated physical processes. Here, we show that such elements can also be used in electronic schemes mimicking biologically-inspired computer architectures, performing digital logic and arithmetic operations, and can expand the capabilities of certain quantum computation schemes. In particular, we will discuss some examples where the concept of memory elements is relevant to the realization of associative memory in neuronal circuits, spike-timing-dependent plasticity of synapses, digital and field-programmable quantum computing.

Complex dynamics and scale invariance of one-dimensional memristive networks

“…We would also like to note that the response of single memristive devices with more complex internal degrees of freedom can be richer than that predicted by Eqs. (2), (3). Even with such single memory devices, one can expect the dependence of the final states on the applied voltage protocol.…”

“…Recently, they have attracted considerable interest in the context of memory applications -where they are oftentimes referred to as memristive systems [2] -but their range of applicability spans various disciplines as diverse as non-traditional computing and biophysics [3][4][5]. In addition to their ubiquity, their theoretical description is quite simple: if a memristive system is subjected to a voltage V (t), its resistance can be written as R(x, V, t), namely it may depend on the voltage itself (which would simply make it a non-linear element), and, most importantly, it depends on some state variable(s), x, which could be, e.g., the spin polarization [6,7] or the position of atomic defects [8], or any other physical property of the system that gives it memory.…”

A comprehensive approach to decipher biological computation to achieve next generation high-performance exascale computing.

“…By engineering a uniform conduction front (UCF), the variability of the resistive switching decreased by as much as 90%, and the window of resistance modulation with controllable linear tunability increased by as much as 75%. Furthermore, the attainment of a UCF increases the memcapacitive properties of the device by as much as 400%, enabling additional applications such as dynamic RF filtering, or efficient digital computation [64].…”

“…Memristive systems, which display hysteretic I-V relationships based on tunable internal state variables, were originally observed and predicted as many as 40 years ago [58,60,61,79], but have recently become a field of intense research [52,55,56,77]. Their inherently coupled ionic and electronic transport properties have provided fundamental research interest [71,80], and their unique switching properties have opened up the potential for new platforms of functionality such as stateful logic operations [81] and neuromorphic computation [62][63][64]. However, in order to realize the aforementioned applications, optimal material systems (and their state variables) must be identified, and quantitative models of the physical mechanisms governing their resistive switching must be developed, allowing the resistive state to be predictively modulated, facilitating their integration into functional systems.…”

Optimal condition of memristance enhancement circuit using external voltage source

“…1, 2 Properties of memristive devices, such as memristors, 2-10 memcapacitors, [11][12][13][14] and meminductors, 15 depend on the history and state of the systems. Such memristive devices have opened up numerous potential applications; digital memories, [16][17][18][19][20] logic circuits, 21 neuromorphic circuits, [22][23][24] learning circuits, 25 programmable circuits, 26 and sensors. 27 These functions are obtained using nonlinearity of the memristive devices.…”