Abhinav Parihar

Pairwise coupled hybrid vanadium dioxide-MOSFET (HVFET) oscillators for non-boolean associative computing

Information processing applications related to associative computing like image / pattern recognition consume excessive computational resources in the Boolean processing framework. This motivates the exploration of a non-Boolean computing approach for such applications. In this work, we demonstrate, (i) novel hybrid set of pair-wise coupled oscillators comprising of vanadium dioxide (VO2) metal-insulator-transition (MIT) system integrated with MOSFET; (ii) degree of synchronization between oscillators based on input analog voltage difference; (iii) implementation of hardware platform for fast and efficient evaluation of Lk fractional distance norm (k<;1); (iv) improved quality of image processing and ~20X lower power consumption of the coupled oscillators over a CMOS accelerator.

Neuro Inspired Computing with Coupled Relaxation Oscillators

Harnessing the computational capabilities of dynamical systems has attracted the attention of scientists and engineers form varied technical disciplines over decades. The time evolution of coupled, non-linear synchronous oscillatory systems has led to active research in understanding their dynamical properties and exploring their applications in brain-inspired, neuromorphic computational models. In this paper we present the realization of coupled and scalable relaxation-oscillators utilizing the metal-insulator-metal transition of vanadium-dioxide (VO2) thin films. We demonstrate the potential use of such a system in pattern recognition, as one possible computational model using such a system.

Synchronization of pairwise-coupled, identical, relaxation oscillators based on metal-insulator phase transition devices: A model study

Computing with networks of synchronous oscillators has attracted wide-spread attention as novel materials and device topologies have enabled realization of compact, scalable and low-power coupled oscillatory systems. Of particular interest are compact and low-power relaxation oscillators that have been recently demonstrated using MIT (metal-insulator-transition) devices using properties of correlated oxides. Further the computational capability of pairwise coupled relaxation oscillators has also been shown to outperform traditional Boolean digital logic circuits. This paper presents an analysis of the dynamics and synchronization of a system of two such identical coupled relaxation oscillators implemented with MIT devices. We focus on two implementations of the oscillator: (a) a D-D configuration where complementary MIT devices (D) are connected in series to provide oscillations and (b) a D-R configuration where it is composed of a resistor (R) in series with a voltage-triggered state changing MIT device ...

Exploiting Synchronization Properties of Correlated Electron Devices in a Non-Boolean Computing Fabric for Template Matching

As complementary metal-oxide-semiconductor (CMOS) scaling continues to offer insurmountable challenges, questions about the performance capabilities of Boolean, digital machine based on Von-Neumann architecture, when operated within a power budget, have also surfaced. Research has started in earnest to identify alternative computing paradigms that provide orders of magnitude improvement in power-performance for specific tasks such as graph traversal, image recognition, template matching, and so on. Further, post-CMOS device technologies have emerged that realize computing elements which are neither CMOS replacements nor suited to work as a binary switch. In this paper, we present the realization of coupled and scalable relaxation-oscillators utilizing the metal-insulator-metal transition of vanadium-dioxide (VO2) thin films. We demonstrate the potential use of such a system in a non-Boolean computing paradigm and demonstrate pattern recognition, as one possible application using such a system.

Vertex coloring of graphs via phase dynamics of coupled oscillatory networks

While Boolean logic has been the backbone of digital information processing, there exist classes of computationally hard problems wherein this paradigm is fundamentally inefficient. Vertex coloring of graphs, belonging to the class of combinatorial optimization, represents one such problem. It is well studied for its applications in data sciences, life sciences, social sciences and technology, and hence, motivates alternate, more efficient non-Boolean pathways towards its solution. Here we demonstrate a coupled relaxation oscillator based dynamical system that exploits insulator-metal transition in Vanadium Dioxide (VO2) to efficiently solve vertex coloring of graphs. Pairwise coupled VO2 oscillator circuits have been analyzed before for basic computing operations, but using complex networks of VO2 oscillators, or any other oscillators, for more complex tasks have been challenging in theory as well as in experiments. The proposed VO2 oscillator network harnesses the natural analogue between optimization problems and energy minimization processes in highly parallel, interconnected dynamical systems to approximate optimal coloring of graphs. We further indicate a fundamental connection between spectral properties of linear dynamical systems and spectral algorithms for graph coloring. Our work not only elucidates a physics-based computing approach but also presents tantalizing opportunities for building customized analog co-processors for solving hard problems efficiently.

Computing with dynamical systems based on insulator-metal-transition oscillators

Abstract In this paper, we review recent work on novel computing paradigms using coupled oscillatory dynamical systems. We explore systems of relaxation oscillators based on linear state transitioning devices, which switch between two discrete states with hysteresis. By harnessing the dynamics of complex, connected systems, we embrace the philosophy of “let physics do the computing” and demonstrate how complex phase and frequency dynamics of such systems can be controlled, programmed, and observed to solve computationally hard problems. Although our discussion in this paper is limited to insulator-to-metallic state transition devices, the general philosophy of such computing paradigms can be translated to other mediums including optical systems. We present the necessary mathematical treatments necessary to understand the time evolution of these systems and demonstrate through recent experimental results the potential of such computational primitives.

A Model Study of Defects and Faults in Embedded Spin Transfer Torque (STT) MRAM Arrays

There has been a significant interest in Spin Transfer Torque Magnetic Random Access Memory (STT-MRAM) as a candidate for emerging memory technology for last-level embedded caches in the recent years. High density (3-4x of SRAM), non-volatility, nano-second Read and Write speeds, and process and voltage compatibility with CMOS are the attractive properties of this technology. A few studies have expounded on the reliability in this technology but various fault manifestations have not been studied in detail in the past. This paper attempts to study the fault models in STT-MRAM under both parametric variations as well as electrical defects (opens and shorts). Sensitivity of Read, Write and Retention to material and lithographic process parameters has been studied. Also electrical defects viz. intra-cell and inter-cell opens and shorts have been considered and the corresponding fault models have been identified and classified.

A random number generator based on insulator-to-metal electronic phase transitions

Random number generators (RNG) are a fundamental hardware component in modern cryptographic systems [1]. The generation of random numbers can be subdivided into two classes, pseudo-RNGs and hardware RNGs. In pseudo-RNGs software algorithms are implemented on deterministic hardware but are dependent on a set of initial values or “seed”, which reduces the security. In contrast, hardware RNGs generate random numbers from a naturally occurring physical phenomenon, such as thermal noise. However, implementations often suffer from large silicon footprints due to the need to create resistor-amplifier-ADC chains [2] or bias removal circuits [3]. In this work, we experimentally demonstrate a compact and scalable 1T1R based RNG by harnessing the inherent stochasticity in the insulator-to-metal phase transition (IMT) in vanadium dioxide (VO2).

Computational paradigms using oscillatory networks based on state-transition devices

In this paper we review recent work on computational paradigms involving coupled relaxation oscillators built using metal-insulator-transition (MIT) devices. Such oscillators made using MIT devices based on Vanadium-Dioxide thin films are very compact and can be realized in hardware. Networks of such oscillators have interesting phase and frequency dynamics which can be programmed to solve computationally hard problems.

Computing with dynamical systems in the post-CMOS era

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.