Low Power Memory/Memristor Devices and Systems

This reprint focusses on achieving low-power computation using memristive devices. The topic was designed as a convenient reference point: it contains a mix of techniques starting from the fundamental manufacturing of memristive devices all the way to applications such as physically unclonable functions, and also covers perspectives on, e.g., in-memory computing, which is inextricably linked with emerging memory devices such as memristors. Finally, the reprint contains a few articles representing how other communities (from typical CMOS design to photonics) are fighting on their own fronts in the quest towards low-power computation, as a comparison with the memristor literature. We hope that readers will enjoy discovering the articles within

Analogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices

Biological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor electronics that rely on the von Neumann architecture. Therefore, novel bio-inspired computing architectures have been attracting a lot of attention in the field of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are employed to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology to neuromorphic systems. However, dealing with the inherent stochasticity of resistive switching can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail, and their potential applications in stochastic artificial neural networks (StochANNs) capable of solving MNIST pattern recognition tasks is examined. A mixed-signal implementation with hardware synapses and software neurons combined with numerical simulations shows that the proposed concept of stochastic computing is able to process analogue data with binary memory cells

Cryogenic Neuromorphic Hardware

The revolution in artificial intelligence (AI) brings up an enormous storage and data processing requirement. Large power consumption and hardware overhead have become the main challenges for building next-generation AI hardware. To mitigate this, Neuromorphic computing has drawn immense attention due to its excellent capability for data processing with very low power consumption. While relentless research has been underway for years to minimize the power consumption in neuromorphic hardware, we are still a long way off from reaching the energy efficiency of the human brain. Furthermore, design complexity and process variation hinder the large-scale implementation of current neuromorphic platforms. Recently, the concept of implementing neuromorphic computing systems in cryogenic temperature has garnered intense interest thanks to their excellent speed and power metric. Several cryogenic devices can be engineered to work as neuromorphic primitives with ultra-low demand for power. Here we comprehensively review the cryogenic neuromorphic hardware. We classify the existing cryogenic neuromorphic hardware into several hierarchical categories and sketch a comparative analysis based on key performance metrics. Our analysis concisely describes the operation of the associated circuit topology and outlines the advantages and challenges encountered by the state-of-the-art technology platforms. Finally, we provide insights to circumvent these challenges for the future progression of research

Novel Computing Paradigms using Oscillators

This dissertation is concerned with new ways of using oscillators to perform computational tasks. Specifically, it introduces methods for building finite state machines (for general-purpose Boolean computation) as well as Ising machines (for solving combinatorial optimization problems) using coupled oscillator networks.But firstly, why oscillators? Why use them for computation?An important reason is simply that oscillators are fascinating. Coupled oscillator systems often display intriguing synchronization phenomena where spontaneous patterns arise. From the synchronous flashing of fireflies to Huygens' clocks ticking in unison, from the molecular mechanism of circadian rhythms to the phase patterns in oscillatory neural circuits, the observation and study of synchronization in coupled oscillators has a long and rich history. Engineers across many disciplines have also taken inspiration from these phenomena, e.g., to design high-performance radio frequency communication circuits and optical lasers. To be able to contribute to the study of coupled oscillators and leverage them in novel paradigms of computing is without question an interesting andfulfilling quest in and of itself.Moreover, as Moore's Law nears its limits, new computing paradigms that are different from mere conventional complementary metal–oxide–semiconductor (CMOS) scaling have become an important area of exploration. One broad direction aims to improve CMOS performance using device technology such as fin field-effect transistors (FinFET) and gate-all-around (GAA) FETs. Other new computing schemes are based on non-CMOS material and device technology, e.g., graphene, carbon nanotubes, memristive devices, optical devices, etc.. Another growing trend in both academia and industry is to build digital application-specific integrated circuits (ASIC) suitable for speeding up certain computational tasks, often leveraging the parallel nature of unconventional non-von Neumann architectures. These schemes seek to circumvent the limitations posed at the device level through innovations at the system/architecture level.Our work on oscillator-based computation represents a direction that is different from the above and features several points of novelty and attractiveness. Firstly, it makes meaningful use of nonlinear dynamical phenomena to tackle well-defined computational tasks that span analog and digital domains. It also differs from conventional computational systems at the fundamental logic encoding level, using timing/phase of oscillation as opposed to voltage levels to represent logic values. These differences bring about several advantages. The change of logic encoding scheme has several device- and system-level benefits related to noise immunity and interference resistance. The use of nonlinear oscillator dynamics allows our systems to address problems difficult for conventional digital computation. Furthermore, our schemes are amenable to realizations using almost all types of oscillators, allowing a wide variety of devices from multiple physical domains to serve as the substrate for computing. This ability to leverage emerging multiphysics devices need not put off the realization of our ideas far into the future. Instead, implementations using well-established circuit technology are already both practical and attractive.This work also differs from all past work on oscillator-based computing, which mostly focuses on specialized image preprocessing tasks, such as edge detection, image segmentation and pattern recognition. Perhaps its most unique feature is that our systems use transitions between analog and digital modes of operation --- unlike other existing schemes that simply couple oscillators and let their phases settle to a continuum of values, we use a special type of injection locking to make each oscillator settle to one of the several well-defined multistable phase-locked states, which we use to encode logic values for computation. Our schemes of oscillator-based Boolean and Ising computation are built upon this digitization of phase; they expand the scope of oscillator-based computing significantly.Our ideas are built on years of past research in the modelling, simulation and analysis of oscillators. While there is a considerable amount of literature (arguably since Christiaan Huygens wrote about his observation of synchronized pendulum clocks in the 17th century) analyzing the synchronization phenomenon from different perspectives at different levels, we have been able to further develop the theory of injection locking, connecting the dots to find a path of analysis that starts from the low-level differential equations of individual oscillators and arrives at phase-based models and energy landscapes of coupled oscillator systems. This theoretical scaffolding is able not only to explain the operation of oscillator-based systems, but also to serve as the basis for simulation and design tools. Building on this, we explore the practical design of our proposed systems, demonstrate working prototypes, as well as develop the techniques, tools and methodologies essential for the process

Adaptation Strategies for Personalized Gait Neuroprosthetics

Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.AK is funded by a faculty endowment by adidas AG. MA, SKH, NM, MN, RJQ, R-DR, RJT are supported by NSF CPS grant 1739800, VA Merit Reviews A2275-R and 3056, and the NIH (5T32EB004314-15, R01 NS040547-13). MS and AG are funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. 803035). AJd-A, JMF-L, and JCM are supported by coordinated grants RTI2018-097290-B-C31/C32/C33 (TAILOR project) funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. MR is funded by the Lo3-ML project by the Federal Ministry for Education, Science and Technology (BMBF) (Funding No. 16ES1142K). AC, SS, and MV were supported by the European Research Council (ERC) under the project NGBMI (759370), the Einstein Stiftung Berlin, the ERA-NET NEURON project HYBRIDMIND (BMBF, 01GP2121A and -B) and the BMBF project NEO (13GW0483C)