Neuromorphic systems, which emulate neural functionalities of a human brain, are considered to be an attractive next‐generation computing approach, with advantages of high energy efficiency and fast computing speed. After these neuromorphic systems are proposed, it is demonstrated that artificial synapses and neurons can mimic neural functions of biological synapses and neurons. However, since the neuromorphic functionalities are highly related to the surface properties of materials, bulk material‐based neuromorphic devices suffer from uncontrollable defects at surfaces and strong scattering caused by dangling bonds. Therefore, 2D materials which have dangling‐bond‐free surfaces and excellent crystallinity have emerged as promising candidates for neuromorphic computing hardware. First, the fundamental synaptic behavior is reviewed, such as synaptic plasticity and learning rule, and requirements of artificial synapses to emulate biological synapses. In addition, an overview of recent advances on 2D materials‐based synaptic devices is summarized by categorizing these into various working principles of artificial synapses. Second, the compulsory behavior and requirements of artificial neurons such as the all‐or‐nothing law and refractory periods to simulate a spike neural network are described, and the implementation of 2D materials‐based artificial neurons to date is reviewed. Finally, future challenges and outlooks of 2D materials‐based neuromorphic devices are discussed.
Machine learning imitates the basic features of biological neural networks at a software level. A strong effort is currently being made to mimic neurons and synapses with hardware components, an approach known as neuromorphic computing. While recent advances in resistive switching have provided a path to emulate synapses at the 10 nm scale, a scalable neuron analogue is yet to be found. Here, we show how heat transfer can be utilized to mimic neuron functionalities in Mott nanodevices. We use the Joule heating created by current spikes to trigger the insulator-to-metal transition in a biased VO2nanogap. We show that thermal dynamics allow the implementation of the basic neuron functionalities: activity, leaky integrate-and-fire, volatility and rate coding. This approach could enable neuromorphic hardware to take full advantage of the rapid advances in memristive synapses, allowing for much denser and complex neural networks.
more » « less- PAR ID:
- 10154400
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
Driven by the expanse of Internet of Things (IoT) and Cyber-Physical Systems (CPS), there is an increasing demand to process streams of temporal data on embedded devices with limited energy and power resources. Among all potential solutions, neuromorphic computing with spiking neural networks (SNN) that mimic the behavior of brain, have recently been placed at the forefront. Encoding information into sparse and distributed spike events enables low-power implementations, and the complex spatial temporal dynamics of synapses and neurons enable SNNs to detect temporal pattern. However, most existing hardware SNN implementations use simplified neuron and synapse models ignoring synapse dynamic, which is critical for temporal pattern detection and other applications that require temporal dynamics. To adopt a more realistic synapse model in neuromorphic platform its significant computation overhead must be addressed. In this work, we propose an FPGA-based SNN with biologically realistic neuron and synapse for temporal information processing. An encoding scheme to convert continuous real-valued information into sparse spike events is presented. The event-driven implementation of synapse dynamic model and its hardware design that is optimized to exploit the sparsity are also presented. Finally, we train the SNN on various temporal pattern-learning tasks and evaluate its performance and efficiency as compared to rate-based models and artificial neural networks on different embedded platforms. Experiments show that our work can achieve 10X speed up and 196X gains in energy efficiency compared with GPU.more » « less
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Abstract Memristors for neuromorphic computing have gained prominence over the years for implementing synapses and neurons due to their nano-scale footprint and reduced complexity. Several demonstrations show two-dimensional (2D) materials as a promising platform for the realization of transparent, flexible, ultra-thin memristive synapses. However, unsupervised learning in a spiking neural network (SNN) facilitated by linearity and symmetry in synaptic weight update has not been explored thoroughly using the 2D materials platform. Here, we demonstrate that graphene/MoS2/SiOx/Ni synapses exhibit ideal linearity and symmetry when subjected to identical input pulses, which is essential for their role in online training of neural networks. The linearity in weight update holds for a range of pulse width, amplitude and number of applied pulses. Our work illustrates that the mechanism of switching in MoS2-based synapses is through conductive filaments governed by Poole-Frenkel emission. We demonstrate that the graphene/MoS2/SiOx/Ni synapses, when integrated with a MoS2-based leaky integrate-and-fire neuron, can control the spiking of the neuron efficiently. This work establishes 2D MoS2as a viable platform for all-memristive SNNs.
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Neuromorphic hardware, designed to mimic the neural structure of the human brain, offers an energy-efficient platform for implementing machine-learning models in the form of Spiking Neural Networks (SNNs). Achieving efficient SNN execution on this hardware requires careful consideration of various objectives, such as optimizing utilization of individual neuromorphic cores and minimizing inter-core communication. Unlike previous approaches that overlooked the architecture of the neuromorphic core when clustering the SNN into smaller networks, our approach uses architecture-aware algorithms to ensure that the resulting clusters can be effectively mapped to the core. We base our approach on a crossbar architecture for each neuromorphic core. We start with a basic architecture where neurons can only be mapped to the columns of the crossbar. Our technique partitions the SNN into clusters of neurons and synapses, ensuring that each cluster fits within the crossbar's confines, and when multiple clusters are allocated to a single crossbar, we maximize resource utilization by efficiently reusing crossbar resources. We then expand this technique to accommodate an enhanced architecture that allows neurons to be mapped not only to the crossbar's columns but also to its rows, with the aim of further optimizing utilization. To evaluate the performance of these techniques, assuming a multi-core neuromorphic architecture, we assess factors such as the number of crossbars used and the average crossbar utilization. Our evaluation includes both synthetically generated SNNs and spiking versions of well-known machine-learning models: LeNet, AlexNet, DenseNet, and ResNet. We also investigate how the structure of the SNN impacts solution quality and discuss approaches to improve it.more » « less
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null (Ed.)Artificial intelligence enabled by neural networks has enabled applications in many fields (e.g. medicine, finance, autonomous vehicles). Software implementations of neural networks on conventional computers are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimic neurons and synapses in brain for distributed and parallel processing. Neuromorphic engineering enabled by silicon photonics can offer subnanosecond latencies, and can extend the domain of artificial intelligence applications to high-performance computing and ultrafast learning. We discuss current progress and challenges on these demonstrations to scale to practical systems for training and inference.more » « less
