Evolutionary 2D organic crystals for optoelectronic transistors and neuromorphic computing

Brain-inspired neuromorphic computing has been extensively researched, taking advantage of increased computer power, the acquisition of massive data, and algorithm optimization. Neuromorphic computing requires mimicking synaptic plasticity and enables near-in-sensor computing. In synaptic transistors, how to elaborate and examine the link between microstructure and characteristics is a major difficulty. Due to the absence of interlayer shielding effects, defect-free interfaces, and wide spectrum responses, reducing the thickness of organic crystals to the 2D limit has a lot of application possibilities in this computing paradigm. This paper presents an update on the progress of 2D organic crystal-based transistors for data storage and neuromorphic computing. The promises and synthesis methodologies of 2D organic crystals are summarized. Following that, applications of 2D organic crystals for ferroelectric nonvolatile memory, circuit-type optoelectronic synapses, and neuromorphic computing are addressed. Finally, new insights and challenges for the field's future prospects are presented, pushing the boundaries of neuromorphic computing even farther.

2022 roadmap on neuromorphic computing and engineering

Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this Roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community.

Digital coded exposure formation of frames from event-based imagery

Event-driven neuromorphic imagers have a number of attractive properties including low-power consumption, high dynamic range, the ability to detect fast events, low memory consumption and low band-width requirements. One of the biggest challenges with using event-driven imagery is that the field of event data processing is still embryonic. In contrast, decades worth of effort have been invested in the analysis of frame-based imagery. Hybrid approaches for applying established frame-based analysis techniques to event-driven imagery have been studied since event-driven imagers came into existence. However, the process for forming frames from event-driven imagery has not been studied in detail. This work presents a principled digital coded exposure approach for forming frames from event-driven imagery that is inspired by the physics exploited in a conventional camera featuring a shutter. The technique described in this work provides a fundamental tool for understanding the temporal information content that contributes to the formation of a frame from event-driven imagery data. Event-driven imagery allows for the application of arbitrary virtual digital shutter functions to form the final frame on a pixel-by-pixel basis. The proposed approach allows for the careful control of the spatio-temporal information that is captured in the frame. Furthermore, unlike a conventional physical camera, event-driven imagery can be formed into any variety of possible frames in post-processing after the data is captured. Furthermore, unlike a conventional physical camera, coded-exposure virtual shutter functions can assume arbitrary values including positive, negative, real, and complex values. The coded exposure approach also enables the ability to perform applications of industrial interest such as digital stroboscopy without any additional hardware. The ability to form frames from event-driven imagery in a principled manner opens up new possibilities in the ability to use conventional frame-based image processing techniques on event-driven imagery.

Ferroelectric-based synapses and neurons for neuromorphic computing

The shift towards a distributed computing paradigm, where multiple systems acquire and elaborate data in real-time, leads to challenges that must be met. In particular, it is becoming increasingly essential to compute on the edge of the network, close to the sensor collecting data. The requirements of a system operating on the edge are very tight: power efficiency, low area occupation, fast response times, and on-line learning. Brain-inspired architectures such as Spiking Neural Networks (SNNs) use artificial neurons and synapses that simultaneously perform low-latency computation and internal-state storage with very low power consumption. Still, they mainly rely on standard complementary metal-oxide-semiconductor (CMOS) technologies, making SNNs unfit to meet the aforementioned constraints. Recently, emerging technologies such as memristive devices have been investigated to flank CMOS technology and overcome edge computing systems' power and memory constraints. In this review, we will focus on ferroelectric technology. Thanks to its CMOS-compatible fabrication process and extreme energy efficiency, ferroelectric devices are rapidly affirming themselves as one of the most promising technology for neuromorphic computing. Therefore, we will discuss their role in emulating neural and synaptic behaviors in an area and power-efficient way.

Evolutionary vs. imitation learning for neuromorphic control at the edge

Neuromorphic computing offers the opportunity to implement extremely low power artificial intelligence at the edge. Control applications, such as autonomous vehicles and robotics, are also of great interest for neuromorphic systems at the edge. It is not clear, however, what the best neuromorphic training approaches are for control applications at the edge. In this work, we implement and compare the performance of evolutionary optimization and imitation learning approaches on an autonomous race car control task using an edge neuromorphic implementation. We show that the evolutionary approaches tend to achieve better performing smaller network sizes that are well-suited to edge deployment, but they also take significantly longer to train. We also describe a workflow to allow for future algorithmic comparisons for neuromorphic hardware on control applications at the edge.

Performance of reservoir computing in a random network of single-walled carbon nanotubes complexed with polyoxometalate

Molecular neuromorphic devices are composed of a random and extremely dense network of single-walled carbon nanotubes (SWNTs) complexed with polyoxometalate (POM). Such devices are expected to have the rudimentary ability of reservoir computing (RC), which utilizes signal response dynamics and a certain degree of network complexity. In this study, we performed RC using multiple signals collected from a SWNT/POM random network. The signals showed a nonlinear response with wide diversity originating from the network complexity. The performance of RC was evaluated for various tasks such as waveform reconstruction, a nonlinear autoregressive model, and memory capacity. The obtained results indicated its high capability as a nonlinear dynamical system, capable of information processing incorporated into edge computing in future technologies.

Two-dimensional molybdenum disulfide artificial synapse with high sensitivity

This paper reports the fabrication of an artificial synapse (AS) based on two-dimensional molybdenum disulfide (MoS2) film. The AS emulates important synaptic functions such as paired-pulse facilitation, spike-rate dependent plasticity, spike-duration dependent plasticity and spike-number dependent plasticity. The spike voltage can mediate ion migration in the ion gel to regulate the MoS2 conductive channel, thereby realizing the emulation of synaptic plasticity. More importantly, benefiting from the atomically-flat surface of MoS2 film, the device has a high sensitivity to external stimuli. It can effectively respond to presynaptic spikes that have an amplitude of 100 mV. The development of this device provides a new idea for constructing a highly-sensitive and multifunctional neuromorphic system.

Stochasticity invariance control in Pr1-xCaxMnO3 RRAM to enable large-scale stochastic recurrent neural networks

Emerging non-volatile memories have been proposed for a wide range of applications, from easing the von-Neumann bottleneck to neuromorphic applications. Specifically, scalable RRAMs based on Pr1-xCaxMnO3 (PCMO) exhibit analog switching have been demonstrated as an integrating neuron, an analog synapse, and a voltage-controlled oscillator. More recently, the inherent stochasticity of memristors has been proposed for efficient hardware implementations of Boltzmann Machines. However, as the problem size scales, the number of neurons increases and controlling the stochastic distribution tightly over many iterations is necessary. This requires parametric control over stochasticity. Here, we characterize the stochastic Set in PCMO RRAMs. We identify that the Set time distribution depends on the internal state of the device (i.e., resistance) in addition to external input (i.e., voltage pulse). This requires the confluence of contradictory properties like stochastic switching as well as deterministic state control in the same device. Unlike ‘stochastic-everywhere’ filamentary memristors, in PCMO RRAMs, we leverage the (i) stochastic Set in negative polarity and (ii) deterministic analog Reset in positive polarity to demonstrate 100× reduced Set time distribution drift. The impact on Boltzmann Machines’ performance is analyzed and as opposed to the “fixed external input stochasticity”, the “state-monitored stochasticity” can solve problems 20× larger in size. State monitoring also tunes out the device-to-device variability effect on distributions providing 10× better performance. In addition to the physical insights, this study establishes the use of experimental stochasticity in PCMO RRAMs in stochastic recurrent neural networks reliably over many iterations.

Holographic photonic neuron

The promise of artificial intelligence (AI) to process complex datasets has brought about innovative computing paradigms. While recent developments in quantum-photonic computing have reached significant feats, mimicking our brain’s ability to recognize images are poorly integrated in these ventures. Here, I incorporate orbital angular momentum (OAM) states in a classical Vander Lugt optical correlator to create the holographic photonic neuron (HoloPheuron). The HoloPheuron can memorize an array of matched filters in a single phase-hologram, which is derived by linking OAM states with elements in the array. Successful correlation is independent of intensity and yields photons with OAM states of lℏ, which can be used as a transmission protocol or qudits for quantum computing. The unique OAM identifier establishes the HoloPheuron as a fundamental AI device for pattern recognition that can be scaled and integrated with other computing platforms to build-up a neuromorphic quantum-photonic processor that mimics the brain

A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain

Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing paradigm aims at the hardware implementation of neural network computations to support novel solutions for building brain-inspired computing systems. Here, we take inspiration from sensory processing in the nervous system of the fruit fly larva. With its strongly limited computational resources of <200 neurons and <1.000 synapses the larval olfactory pathway employs fundamental computations to transform broadly tuned receptor input at the periphery into an energy efficient sparse code in the central brain. We show how this approach allows us to achieve sparse coding and increased separability of stimulus patterns in a spiking neural network, validated with both software simulation and hardware emulation on mixed-signal real-time neuromorphic hardware. We verify that feedback inhibition is the central motif to support sparseness in the spatial domain, across the neuron population, while the combination of spike frequency adaptation and feedback inhibition determines sparseness in the temporal domain. Our experiments demonstrate that such small-sized, biologically realistic neural networks, efficiently implemented on neuromorphic hardware, can achieve parallel processing and efficient encoding of sensory input at full temporal resolution.