Energy landscapes of combinatorial optimization in Ising machines.

Physics-based Ising machines (IM) have been developed as dedicated processors for solving hard combinatorial optimization problems with higher speed and better energy efficiency. Generally, such systems employ local search heuristics to traverse energy landscapes in searching for optimal solutions. Here, we quantify and address some of the major challenges met by IMs by extending energy-landscape geometry visualization tools known as disconnectivity graphs.

Computing high-degree polynomial gradients in memory.

Specialized function gradient computing hardware could greatly improve the performance of state-of-the-art optimization algorithms. Prior work on such hardware, performed in the context of Ising Machines and related concepts, is limited to quadratic polynomials and not scalable to commonly used higher-order functions. Here, we propose an approach for massively parallel gradient calculations of high-degree polynomials, which is conducive to efficient mixed-signal in-memory computing circuit implementations and whose area scales proportionally with the product of the number of variables and terms in the function and, most importantly, independent of its degree.

An analytical approach to engineer multistability in the oscillatory response of a pulse-driven ReRAM.

A nonlinear system, exhibiting a unique asymptotic behaviour, while being continuously subject to a stimulus from a certain class, is said to suffer from fading memory. This interesting phenomenon was first uncovered in a non-volatile tantalum oxide-based memristor from Hewlett Packard Labs back in 2016 out of a deep numerical investigation of a predictive mathematical description, known as the Strachan model, later corroborated by experimental validation. It was then found out that fading memory is ubiquitous in non-volatile resistance switching memories.

Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search.

Lifelong on-device learning is a key challenge for machine intelligence, and this requires learning from few, often single, samples. Memory-augmented neural networks have been proposed to achieve the goal, but the memory module must be stored in off-chip memory, heavily limiting the practical use. In this work, we experimentally validated that all different structures in the memory-augmented neural network can be implemented in a fully integrated memristive crossbar platform with an accuracy that closely matches digital hardware.

Tree-based machine learning performed in-memory with memristive analog CAM.

Tree-based machine learning techniques, such as Decision Trees and Random Forests, are top performers in several domains as they do well with limited training datasets and offer improved interpretability compared to Deep Neural Networks (DNN). However, these models are difficult to optimize for fast inference at scale without accuracy loss in von Neumann architectures due to non-uniform memory access patterns. Recently, we proposed a novel analog content addressable memory (CAM) based on emerging memristor devices for fast look-up table operations.

In-Memory Computing with Memristor Content Addressable Memories for Pattern Matching.

The dramatic rise of data-intensive workloads has revived application-specific computational hardware for continuing speed and power improvements, frequently achieved by limiting data movement and implementing "in-memory computation". However, conventional complementary metal oxide semiconductor (CMOS) circuit designs can still suffer low power efficiency, motivating designs leveraging nonvolatile resistive random access memory (ReRAM), and with many studies focusing on crossbar circuit architectures. Another circuit primitive-content addressable memory (CAM)-shows great promise for mapping a diverse range of computational models for in-memory computation, with recent ReRAM-CAM designs proposed but few experimentally demonstrated.

Analog content-addressable memories with memristors.

A content-addressable memory compares an input search word against all rows of stored words in an array in a highly parallel manner. While supplying a very powerful functionality for many applications in pattern matching and search, it suffers from large area, cost and power consumption, limiting its use. Past improvements have been realized by using memristors to replace the static random-access memory cell in conventional designs, but employ similar schemes based only on binary or ternary states for storage and search.

Capacitive neural network with neuro-transistors.

Experimental demonstration of resistive neural networks has been the recent focus of hardware implementation of neuromorphic computing. Capacitive neural networks, which call for novel building blocks, provide an alternative physical embodiment of neural networks featuring a lower static power and a better emulation of neural functionalities. Here, we develop neuro-transistors by integrating dynamic pseudo-memcapacitors as the gates of transistors to produce electronic analogs of the soma and axon of a neuron, with "leaky integrate-and-fire" dynamics augmented by a signal gain on the output.

Efficient and self-adaptive in-situ learning in multilayer memristor neural networks.

Memristors with tunable resistance states are emerging building blocks of artificial neural networks. However, in situ learning on a large-scale multiple-layer memristor network has yet to be demonstrated because of challenges in device property engineering and circuit integration. Here we monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer neural network.

Memristor-Based Analog Computation and Neural Network Classification with a Dot Product Engine.

Using memristor crossbar arrays to accelerate computations is a promising approach to efficiently implement algorithms in deep neural networks. Early demonstrations, however, are limited to simulations or small-scale problems primarily due to materials and device challenges that limit the size of the memristor crossbar arrays that can be reliably programmed to stable and analog values, which is the focus of the current work. High-precision analog tuning and control of memristor cells across a 128 × 64 array is demonstrated, and the resulting vector matrix multiplication (VMM) computing precision is evaluated.

Physical origins of current and temperature controlled negative differential resistances in NbO.

Negative differential resistance behavior in oxide memristors, especially those using NbO, is gaining renewed interest because of its potential utility in neuromorphic computing. However, there has been a decade-long controversy over whether the negative differential resistance is caused by a relatively low-temperature non-linear transport mechanism or a high-temperature Mott transition. Resolving this issue will enable consistent and robust predictive modeling of this phenomenon for different applications.

Volatile HRS asymmetry and subloops in resistive switching oxides.

Current-voltage characteristics of oxide-based resistive switching memories often show a pronounced asymmetry with respect to the voltage polarity in the high resistive state (HRS), where the HRS after the RESET is more conducting than the one before the SET. Here, we report that most of this HRS asymmetry is a volatile effect as the HRS obtained from a read operation differs from the one taken from the switching cycle at identical polarity and voltages. Transitions between the relaxed and the volatile excited states can be achieved via voltage sweeps, which are named subloops.

Chaotic dynamics in nanoscale NbO Mott memristors for analogue computing.

At present, machine learning systems use simplified neuron models that lack the rich nonlinear phenomena observed in biological systems, which display spatio-temporal cooperative dynamics. There is evidence that neurons operate in a regime called the edge of chaos that may be central to complexity, learning efficiency, adaptability and analogue (non-Boolean) computation in brains. Neural networks have exhibited enhanced computational complexity when operated at the edge of chaos, and networks of chaotic elements have been proposed for solving combinatorial or global optimization problems.

An efficient analog Hamming distance comparator realized with a unipolar memristor array: a showcase of physical computing.

We propose and demonstrate a novel physical computing paradigm based on an engineered unipolar memristor that exhibits symmetric SET switching with respect to voltage polarity. A one-dimensional array of these devices was sufficient to demonstrate an efficient Hamming distance comparator for two strings of analog states represented by voltages from the physical world. The comparator first simultaneously applies the two sets of voltages to the array of memristors, each of which is initially in its high resistance state and switches to its low resistance state only if the two voltages applied on that memristor differ by more than the switching threshold.

Conduction Channel Formation and Dissolution Due to Oxygen Thermophoresis/Diffusion in Hafnium Oxide Memristors.

Transition-metal-oxide memristors, or resistive random-access memory (RRAM) switches, are under intense development for storage-class memory because of their favorable operating power, endurance, speed, and density. Their commercial deployment critically depends on predictive compact models based on understanding nanoscale physicochemical forces, which remains elusive and controversial owing to the difficulties in directly observing atomic motions during resistive switching, Here, using scanning transmission synchrotron X-ray spectromicroscopy to study in situ switching of hafnium oxide memristors, we directly observed the formation of a localized oxygen-deficiency-derived conductive channel surrounded by a low-conductivity ring of excess oxygen. Subsequent thermal annealing homogenized the segregated oxygen, resetting the cells toward their as-grown resistance state.

Spatially uniform resistance switching of low current, high endurance titanium-niobium-oxide memristors.

We analyzed micrometer-scale titanium-niobium-oxide prototype memristors, which exhibited low write-power (<3 μW) and energy (<200 fJ per bit per μm), low read-power (∼nW), and high endurance (>millions of cycles). To understand their physico-chemical operating mechanisms, we performed in operando synchrotron X-ray transmission nanoscale spectromicroscopy using an ultra-sensitive time-multiplexed technique. We observed only spatially uniform material changes during cell operation, in sharp contrast to the frequently detected formation of a localized conduction channel in transition-metal-oxide memristors.

Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing.

The accumulation and extrusion of Ca in the pre- and postsynaptic compartments play a critical role in initiating plastic changes in biological synapses. To emulate this fundamental process in electronic devices, we developed diffusive Ag-in-oxide memristors with a temporal response during and after stimulation similar to that of the synaptic Ca dynamics. In situ high-resolution transmission electron microscopy and nanoparticle dynamics simulations both demonstrate that Ag atoms disperse under electrical bias and regroup spontaneously under zero bias because of interfacial energy minimization, closely resembling synaptic influx and extrusion of Ca, respectively.

A terrestrial planet candidate in a temperate orbit around Proxima Centauri.

At a distance of 1.295 parsecs, the red dwarf Proxima Centauri (α Centauri C, GL 551, HIP 70890 or simply Proxima) is the Sun's closest stellar neighbour and one of the best-studied low-mass stars. It has an effective temperature of only around 3,050 kelvin, a luminosity of 0.

Repeatable, accurate, and high speed multi-level programming of memristor 1T1R arrays for power efficient analog computing applications.

Beyond use as high density non-volatile memories, memristors have potential as synaptic components of neuromorphic systems. We investigated the suitability of tantalum oxide (TaOx) transistor-memristor (1T1R) arrays for such applications, particularly the ability to accurately, repeatedly, and rapidly reach arbitrary conductance states. Programming is performed by applying an adaptive pulsed algorithm that utilizes the transistor gate voltage to control the SET switching operation and increase programming speed of the 1T1R cells.

Dietary intervention for people with mental illness in South Australia.

Background: People with serious mental illness (SMI) have a 25-30 year lower life expectancy than the general population due largely to cardiovascular disease (CVD). Mediterranean diet can reduce CVD risk and repeat events by 30-70%. We conducted a pilot feasibility study (HELFIMED) with people who have SMI residing within a Community Rehabilitation Centre in South Australia, aimed at improving participants' diets according to Mediterranean diet principles.

People with schizophrenia and depression have a low omega-3 index.

Cardiovascular disease (CVD) is higher in people with mental illness and is associated with a 30 year higher mortality rate in this population. Erythrocyte docosahexaenoic acid (DHA) plus eicosapentaenoic acid (EPA) (omega-3 index)≤4% is a marker for increased mortality risk from CVD while >8% is protective. Omega-3 polyunsaturated fatty acids are also important for brain function and may ameliorate symptoms of mental illness.

Direct Observation of Localized Radial Oxygen Migration in Functioning Tantalum Oxide Memristors.

Oxygen migration in tantalum oxide, a promising next-generation storage material, is studied using in operando X-ray absorption spectromicroscopy. This approach allows a physical description of the evolution of conduction channel and eventual device failure. The observed ring-like patterns of oxygen concentration are modeled using thermophoretic forces and Fick diffusion, establishing the critical role of temperature-driven oxygen migration.

Voltage divider effect for the improvement of variability and endurance of TaO(x) memristor.

The impact of a series resistor (R(S)) on the variability and endurance performance of memristor was studied in the TaO(x) memristive system. A dynamic voltage divider between the R(S) and memristor during both the set and the reset switching cycles can suppress the inherent irregularity of the voltage dropped on the memristor, resulting in a greatly reduced switching variability. By selecting the proper resistance value of R(S) for the set and reset cycles respectively, we observed a dramatically improved endurance of the TaO(x) memristor.