Artificial intelligence (AI) has the ability of revolutionizing our lives and society in a radical way, by enabling machine learning in the industry, business, health, transportation, and many other fields. The ability to recognize objects, faces, and speech, requires, however, exceptional computational power and time, which is conflicting with the current difficulties in transistor scaling due to physical and architectural limitations. As a result, to accelerate the progress of AI, it is necessary to develop materials, devices, and systems that closely mimic the human brain. In this work, we review the current status and challenges on the emerging neuromorphic devices for brain-inspired computing. First, we provide an overview of the memory device technologies which have been proposed for synapse and neuron circuits in neuromorphic systems. Then, we describe the implementation of synaptic learning in the two main types of neural networks, namely the deep neural network and the spiking neural network (SNN). Bio-inspired learning, such as the spike-timing dependent plasticity scheme, is shown to enable unsupervised learning processes which are typical of the human brain. Hardware implementations of SNNs for the recognition of spatial and spatio-temporal patterns are also shown to support the cognitive computation in silico. Finally, we explore the recent advances in reproducing bio-neural processes via device physics, such as insulating-metal transitions, nanoionics drift/diffusion, and magnetization flipping in spintronic devices. By harnessing the device physics in emerging materials, neuromorphic engineering with advanced functionality, higher density and better energy efficiency can be developed.

https://iopscience.iop.org/article/10.1088/1361-6528/ab554b/pdf

Emerging neuromorphic devices.

Neuromorphic computation is one of the axes of parallel distributed processing, and memristor-based synaptic weight is considered as a key component of this type of computation. However, the material properties of memristors, including material related physics, are not yet matured. In parallel with memristors, CMOS based Graphics Processing Unit, Field Programmable Gate Array, and Application Specific Integrated Circuit are also being developed as dedicated artificial intelligence (AI) chips for fast computation. Therefore, it is necessary to analyze the competitiveness of the memristor-based neuromorphic device in order to position the memristor in the appropriate position of the future AI ecosystem. In this article, the status of memristor-based neuromorphic computation was analyzed on the basis of papers and patents to identify the competitiveness of the memristor properties by reviewing industrial trends and academic pursuits. In addition, material issues and challenges are discussed for implementing the memristor-based neural processor.

/pdf/perspective-a-review-on-memristive-hardware-for-neuromorphic-3m78nvsruq.pdf

Perspective: A review on memristive hardware for neuromorphic computation

Memristive devices are promising candidates to emulate biological computing. However, the typical switching voltages (0.2-2 V) in previously described devices are much higher than the amplitude in biological counterparts. Here we demonstrate a type of diffusive memristor, fabricated from the protein nanowires harvested from the bacterium Geobacter sulfurreducens, that functions at the biological voltages of 40-100 mV. Memristive function at biological voltages is possible because the protein nanowires catalyze metallization. Artificial neurons built from these memristors not only function at biological action potentials (e.g., 100 mV, 1 ms) but also exhibit temporal integration close to that in biological neurons. The potential of using the memristor to directly process biosensing signals is also demonstrated. Designing energy efficient systems capable to directly process signals at biological voltages remains a challenge. Here, the authors propose a bio-compatible memristor device based on protein-nanowire dielectric, harvested from the bacterium Geobactor sulfurreducens, working at biological voltages.

/pdf/bioinspired-bio-voltage-memristors-57r6x0dgh8.pdf

Bioinspired bio-voltage memristors

Abstract Neuromorphic computing memristors are attractive to construct low-power- consumption electronic textiles due to the intrinsic interwoven architecture and promising applications in wearable electronics. Developing reconfigurable fiber-based memristors is an efficient method to realize electronic textiles that capable of neuromorphic computing function. However, the previously reported artificial synapse and neuron need different materials and configurations, making it difficult to realize multiple functions in a single device. Herein, a textile memristor network of Ag/MoS 2 /HfAlO x /carbon nanotube with reconfigurable characteristics was reported, which can achieve both nonvolatile synaptic plasticity and volatile neuron functions. In addition, a single reconfigurable memristor can realize integrate-and-fire function, exhibiting significant advantages in reducing the complexity of neuron circuits. The firing energy consumption of fiber-based memristive neuron is 1.9 fJ/spike (femtojoule-level), which is at least three orders of magnitude lower than that of the reported biological and artificial neuron (picojoule-level). The ultralow energy consumption makes it possible to create an electronic neural network that reduces the energy consumption compared to human brain. By integrating the reconfigurable synapse, neuron and heating resistor, a smart textile system is successfully constructed for warm fabric application, providing a unique functional reconfiguration pathway toward the next-generation in-memory computing textile system. 

/pdf/reconfigurable-neuromorphic-memristor-network-for-ultralow-3cjpv2h1.pdf

Reconfigurable neuromorphic memristor network for ultralow-power smart textile electronics

In this article, we review the existing analog resistive switching memory (RSM) devices and their hardware technologies for in-memory learning, as well as their challenges and prospects. Since the characteristics of the devices are different for in-memory learning and digital memory applications, it is important to have an in-depth understanding across different layers from devices and circuits to architectures and algorithms. First, based on a top-down view from architecture to devices for analog computing, we define the main figures of merit (FoMs) and perform a comprehensive analysis of analog RSM hardware including the basic device characteristics, hardware algorithms, and the corresponding mapping methods for device arrays, as well as the architecture and circuit design considerations for neural networks. Second, we classify the FoMs of analog RSM devices into two levels. Level 1 FoMs are essential for achieving the functionality of a system (e.g., linearity, symmetry, dynamic range, level numbers, fluctuation, variability, and yield). Level 2 FoMs are those that make a functional system more efficient and reliable (e.g., area, operational voltage, energy consumption, speed, endurance, retention, and compatibility with back-end-of-line processing). By constructing a device-to-application simulation framework, we perform an in-depth analysis of how these FoMs influence in-memory learning and give a target list of the device requirements. Lastly, we evaluate the main FoMs of most existing devices with analog characteristics and review optimization methods from programming schemes to materials and device structures. The key challenges and prospects from the device to system level for analog RSM devices are discussed.

In-memory Learning with Analog Resistive Switching Memory: A Review and Perspective

Resistance random access memories (RRAM) or memristors with an analog change of conductance are widely explored as an artificial synapse, e.g., Pr0.7Ca0.3MnO3 (PCMO) RRAM-based synapses. In addition to synapses, scaled neurons are essential to enable a neuromorphic hardware. In this letter, we propose a PCMO RRAM for integrate and fire (IF) neuron. The analog conductance increase during SET process enables integration function. Upon exceeding a conductance threshold (i.e., fire) during a READ operation, a hard RESET is performed to reduce the conductance. The SET, READ, and RESET are performed in different phases of a clock to enable a PCMO for IF neuron. The availability of a non-volatile PCMO-based synapse makes PCMO for IF neuron attractive. Finally, PCMO-based neuron in spiking neural network yields software-equivalent classification accuracy as demonstrated on standard Fischer’s Iris flower data set.

PCMO RRAM for Integrate-and-Fire Neuron in Spiking Neural Networks

In Spiking Neural Network (SNN), an Integrate and Fire (IF) neuron or Leaky IF neuron (LIF) is widely used for its small and simple circuitry. However, cortical neurons which are the fundamental computational centers in the brain, exhibit complex spiking patterns like chattering and bursting. Mimicking such variety of spiking patterns of a cortical neuron as modeled by Izhikevich is still a challenge. In this paper, utilizing the unique property of gradual reset and abrupt set in Pr 0.3 Ca 0.7 MnO 3 (PCMO) Resistance Random Access Memory (RRAM), a scheme for physical realization of general Izhikevich dynamics (i.e., spiking and bursting) is proposed and implemented successfully in experiments. Such a neuron with diverse spiking patterns opens up a path for high throughput neuromorphic computing which includes selective communications via resonance.

General Izhikevich Dynamics In PR 0.7 CA 0.3 MNO 3 RRAM Neuron

PrMnO3 (PMO) based Resistance Random Access Memory (RRAM) has recently been considered for selector-less RRAM and neuromorphic computing applications by utilizing its current shoot-up. This current shoot-up in the PMO device is attributed to the thermal runaway in the device. Hence, the understanding of the ambient temperature dependence on the current shoot-up of the PMO device is essential for the various applications that utilize the negative differential resistance (NDR). In this paper, we characterize the ambient thermal dependence of dc IV, accompanied by the development of analytical modeling. First, the temperature-dependent current-voltage characteristic and shift in the threshold voltage of the PMO device are shown experimentally. Second, a Joule heating based thermal feedback model coupled with current transport by space charge limited current (SCLC) is developed to explain the experimentally observed NDR region. Finally, the model successfully predicts device behavior over a range of experimental ambient temperatures. As an alternative to TCAD, such a compact and accurate dc model sets up a platform to enable understanding, design with device and systems-level simulations of memory and neuromorphic applications.

Temperature Dependence of Volatile Current shoot-up in PrMnO3 based Selector-less RRAM.

Hardware realization of scalable neurons and synapses are essential for the implementation of large scale Spiking neural network (SNN). In this paper, first, we propose, a novel transient Joule heating based the leaky-integrate and fire neuron (LIF) in scalable PrMnO3 (PMO) RRAM device experimentally. The Joule-heating based thermal runaway is utilized to achieve rectified linear unit (ReLU) voltage dependence of spiking frequency similar to a typical LIF neuron. Second, the Jouleheating hypothesis in PMO is validated by TCAD DC and transient simulations. PMO is extremely thermally resistive semiconductor (300x cf. Si) and hence enables low energy thermal dynamics. The excellent energy, area performance with a synapse in the same material system and thermal engineering makes PMO neuron attractive. Finally, PMO neuron shows software equivalent learning accuracy in SNN on the Fischer’s Iris dataset.

Transient Joule Heating in PrMnO3 RRAM enables ReLU type Neuron

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Papers

PCMO RRAM for Integrate-and-Fire Neuron in Spiking Neural Networks

General Izhikevich Dynamics In PR 0.7 CA 0.3 MNO 3 RRAM Neuron

Temperature Dependence of Volatile Current shoot-up in PrMnO3 based Selector-less RRAM.

Transient Joule Heating in PrMnO3 RRAM enables ReLU type Neuron