Demonstration of 4-quadrant analog in-memory matrix multiplication in a single modulation.

Analog in-memory computing (AIMC) leverages the inherent physical characteristics of resistive memory devices to execute computational operations, notably matrix-vector multiplications (MVMs). However, executing MVMs using a single-phase reading scheme to reduce latency necessitates the simultaneous application of both positive and negative voltages across resistive memory devices. This degrades the accuracy of the computation due to the dependence of the device conductance on the voltage polarity.

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators.

Analog in-memory computing-a promising approach for energy-efficient acceleration of deep learning workloads-computes matrix-vector multiplications but only approximately, due to nonidealities that often are non-deterministic or nonlinear. This can adversely impact the achievable inference accuracy. Here, we develop an hardware-aware retraining approach to systematically examine the accuracy of analog in-memory computing across multiple network topologies, and investigate sensitivity and robustness to a broad set of nonidealities.

Optimised weight programming for analogue memory-based deep neural networks.

Analogue memory-based deep neural networks provide energy-efficiency and per-area throughput gains relative to state-of-the-art digital counterparts such as graphics processing units. Recent advances focus largely on hardware-aware algorithmic training and improvements to circuits, architectures, and memory devices. Optimal translation of software-trained weights into analogue hardware weights-given the plethora of complex memory non-idealities-represents an equally important task.

Memristive technologies for data storage, computation, encryption, and radio-frequency communication.

Memristive devices, which combine a resistor with memory functions such that voltage pulses can change their resistance (and hence their memory state) in a nonvolatile manner, are beginning to be implemented in integrated circuits for memory applications. However, memristive devices could have applications in many other technologies, such as non-von Neumann in-memory computing in crossbar arrays, random number generation for data security, and radio-frequency switches for mobile communications. Progress toward the integration of memristive devices in commercial solid-state electronic circuits and other potential applications will depend on performance and reliability challenges that still need to be addressed, as described here.

Mechanism and Impact of Bipolar Current Voltage Asymmetry in Computational Phase-Change Memory.

Nanoscale resistive memory devices are being explored for neuromorphic and in-memory computing. However, non-ideal device characteristics of read noise and resistance drift pose significant challenges to the achievable computational precision. Here, it is shown that there is an additional non-ideality that can impact computational precision, namely the bias-polarity-dependent current flow.

Experimental validation of state equations and dynamic route maps for phase change memristive devices.

Phase Change Memory (PCM) is an emerging technology exploiting the rapid and reversible phase transition of certain chalcogenides to realize nanoscale memory elements. PCM devices are being explored as non-volatile storage-class memory and as computing elements for in-memory and neuromorphic computing. It is well-known that PCM exhibits several characteristics of a memristive device.

Robust high-dimensional memory-augmented neural networks.

Traditional neural networks require enormous amounts of data to build their complex mappings during a slow training procedure that hinders their abilities for relearning and adapting to new data. Memory-augmented neural networks enhance neural networks with an explicit memory to overcome these issues. Access to this explicit memory, however, occurs via soft read and write operations involving every individual memory entry, resulting in a bottleneck when implemented using the conventional von Neumann computer architecture.

Publisher Correction: Memory devices and applications for in-memory computing.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Mixed-Precision Deep Learning Based on Computational Memory.

Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has motivated the search for novel computing architectures targeting this application. A computational memory unit with nanoscale resistive memory devices organized in crossbar arrays could store the synaptic weights in their conductance states and perform the expensive weighted summations in place in a non-von Neumann manner.

State dependence and temporal evolution of resistance in projected phase change memory.

Phase change memory (PCM) is being actively explored for in-memory computing and neuromorphic systems. The ability of a PCM device to store a continuum of resistance values can be exploited to realize arithmetic operations such as matrix-vector multiplications or to realize the synaptic efficacy in neural networks. However, the resistance variations arising from structural relaxation, 1/f noise, and changes in ambient temperature pose a key challenge.

Accurate deep neural network inference using computational phase-change memory.

In-memory computing using resistive memory devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. However, due to device variability and noise, the network needs to be trained in a specific way so that transferring the digitally trained weights to the analog resistive memory devices will not result in significant loss of accuracy. Here, we introduce a methodology to train ResNet-type convolutional neural networks that results in no appreciable accuracy loss when transferring weights to phase-change memory (PCM) devices.

Experimental Demonstration of Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses.

Spiking neural networks (SNN) are computational models inspired by the brain's ability to naturally encode and process information in the time domain. The added temporal dimension is believed to render them more computationally efficient than the conventional artificial neural networks, though their full computational capabilities are yet to be explored. Recently, in-memory computing architectures based on non-volatile memory crossbar arrays have shown great promise to implement parallel computations in artificial and spiking neural networks.

Memory devices and applications for in-memory computing.

Traditional von Neumann computing systems involve separate processing and memory units. However, data movement is costly in terms of time and energy and this problem is aggravated by the recent explosive growth in highly data-centric applications related to artificial intelligence. This calls for a radical departure from the traditional systems and one such non-von Neumann computational approach is in-memory computing.

Neuromorphic computing with multi-memristive synapses.

Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could efficiently represent the synaptic weights in artificial neural networks. However, precise modulation of the device conductance over a wide dynamic range, necessary to maintain high network accuracy, is proving to be challenging.

Monatomic phase change memory.

Phase change memory has been developed into a mature technology capable of storing information in a fast and non-volatile way, with potential for neuromorphic computing applications. However, its future impact in electronics depends crucially on how the materials at the core of this technology adapt to the requirements arising from continued scaling towards higher device densities. A common strategy to fine-tune the properties of phase change memory materials, reaching reasonable thermal stability in optical data storage, relies on mixing precise amounts of different dopants, resulting often in quaternary or even more complicated compounds.

Temporal correlation detection using computational phase-change memory.

Conventional computers based on the von Neumann architecture perform computation by repeatedly transferring data between their physically separated processing and memory units. As computation becomes increasingly data centric and the scalability limits in terms of performance and power are being reached, alternative computing paradigms with collocated computation and storage are actively being sought. A fascinating such approach is that of computational memory where the physics of nanoscale memory devices are used to perform certain computational tasks within the memory unit in a non-von Neumann manner.

Stochastic phase-change neurons.

Artificial neuromorphic systems based on populations of spiking neurons are an indispensable tool in understanding the human brain and in constructing neuromimetic computational systems. To reach areal and power efficiencies comparable to those seen in biological systems, electroionics-based and phase-change-based memristive devices have been explored as nanoscale counterparts of synapses. However, progress on scalable realizations of neurons has so far been limited.

Eosinophilic Gastroenteritis: Case Report and Review in Search for Diagnostic Key Points.

Eosinophilic gastroenteritis is considered an uncommon disease with a low incidence rate that remains as a diagnostic challenge for the clinician, in spite of the fact that seventy years have passed since its original description. Hereby we present the case of a 29-year-old male without history of allergies who was evaluated for unspecific gastrointestinal symptoms, without relevant findings on physical examination and presenting an initial complete blood count (CBC) with severe eosinophilia. The patient was evaluated and the diagnosis of eosinophilic gastroenteritis was confirmed by histopathological findings.

Crystal growth within a phase change memory cell.

In spite of the prominent role played by phase change materials in information technology, a detailed understanding of the central property of such materials, namely the phase change mechanism, is still lacking mostly because of difficulties associated with experimental measurements. Here, we measure the crystal growth velocity of a phase change material at both the nanometre length and the nanosecond timescale using phase-change memory cells. The material is studied in the technologically relevant melt-quenched phase and directly in the environment in which the phase change material is going to be used in the application.

Amniotic fluid iodine concentrations do not vary in pregnant women with varying iodine intake.

Iodine deficiency is an important clinical and public health problem. Its prevention begins with an adequate intake of iodine during pregnancy. International agencies recommend at least 200 microg iodine per d for pregnant women.