DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays.

An increasing number of studies are highlighting the importance of spatial dendritic branching in pyramidal neurons in the neocortex for supporting non-linear computation through localized synaptic integration. In particular, dendritic branches play a key role in temporal signal processing and feature detection. This is accomplished thanks to coincidence detection (CD) mechanisms enabled by the presence of synaptic delays that align temporally disparate inputs for effective integration. Computational studies on spiking neural networks further highlight the significance of delays for achieving spatio-temporal pattern recognition with pure feed-forward neural networks, without the need of resorting to recurrent architectures. In this work, we present "DenRAM", the first realization of a feed-forward spiking neural network with dendritic compartments, implemented using analog electronic circuits integrated into a 130 nm technology node and coupled with Resistive Random Access Memory (RRAM) technology. DenRAM's dendritic circuits use RRAM devices to implement both delays and synaptic weights in the network. By configuring the RRAM devices to reproduce bio-realistic timescales, and by exploiting their heterogeneity we experimentally demonstrate DenRAM's ability to replicate synaptic delay profiles, and to efficiently implement CD for spatio-temporal pattern recognition. To validate the architecture, we conduct comprehensive system-level simulations on two representative temporal benchmarks, demonstrating DenRAM's resilience to analog hardware noise, and its superior accuracy compared to recurrent architectures with an equivalent number of parameters. DenRAM not only brings rich temporal processing capabilities to neuromorphic architectures, but also reduces the memory footprint of edge devices, warrants high accuracy on temporal benchmarks, and represents a significant step-forward in low-power real-time signal processing technologies.

Mosaic: in-memory computing and routing for small-world spike-based neuromorphic systems.

The brain's connectivity is locally dense and globally sparse, forming a small-world graph-a principle prevalent in the evolution of various species, suggesting a universal solution for efficient information routing. However, current artificial neural network circuit architectures do not fully embrace small-world neural network models. Here, we present the neuromorphic Mosaic: a non-von Neumann systolic architecture employing distributed memristors for in-memory computing and in-memory routing, efficiently implementing small-world graph topologies for Spiking Neural Networks (SNNs). We've designed, fabricated, and experimentally demonstrated the Mosaic's building blocks, using integrated memristors with 130 nm CMOS technology. We show that thanks to enforcing locality in the connectivity, routing efficiency of Mosaic is at least one order of magnitude higher than other SNN hardware platforms. This is while Mosaic achieves a competitive accuracy in a variety of edge benchmarks. Mosaic offers a scalable approach for edge systems based on distributed spike-based computing and in-memory routing.

Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing.

Many in-memory computing frameworks demand electronic devices with specific switching characteristics to achieve the desired level of computational complexity. Existing memristive devices cannot be reconfigured to meet the diverse volatile and non-volatile switching requirements, and hence rely on tailored material designs specific to the targeted application, limiting their universality. "Reconfigurable memristors" that combine both ionic diffusive and drift mechanisms could address these limitations, but they remain elusive. Here we present a reconfigurable halide perovskite nanocrystal memristor that achieves on-demand switching between diffusive/volatile and drift/non-volatile modes by controllable electrochemical reactions. Judicious selection of the perovskite nanocrystals and organic capping ligands enable state-of-the-art endurance performances in both modes - volatile (2 × 106 cycles) and non-volatile (5.6 × 103 cycles). We demonstrate the relevance of such proof-of-concept perovskite devices on a benchmark reservoir network with volatile recurrent and non-volatile readout layers based on 19,900 measurements across 25 dynamically-configured devices.

Tailoring far-infrared surface plasmon polaritons of a single-layer graphene using plasmon-phonon hybridization in graphene-LiF heterostructures.

Being one-atom thick and tunable simultaneously, graphene plays the revolutionizing role in many areas. The focus of this paper is to investigate the modal characteristics of surface waves in structures with graphene in the far-infrared (far-IR) region. We discuss the effects exerted by substrate permittivity on propagation and localization characteristics of surface-plasmon-polaritons (SPPs) in single-layer graphene and theoretically investigate characteristics of the hybridized surface-phonon-plasmon-polaritons (SPPPs) in graphene/LiF/glass heterostructures. First, it is shown how high permittivity of substrate may improve characteristics of graphene SPPs. Next, the possibility of optimization for surface-phonon-polaritons (SPhPs) in waveguides based on LiF, a polar dielectric with a wide polaritonic gap (Reststrahlen band) and a wide range of permittivity variation, is demonstrated. Combining graphene and LiF in one heterostructure allows to keep the advantages of both, yielding tunable hybridized SPPPs which can be either forwardly or backwardly propagating. Owing to high permittivity of LiF below the gap, an almost 3.2-fold enhancement in the figure of merit (FoM), ratio of normalized propagation length to localization length of the modes, can be obtained for SPPPs at 5-9 THz, as compared with SPPs of graphene on conventional glass substrate. The enhancement is efficiently tunable by varying the chemical potential of graphene. SPPPs with characteristics which strongly differ inside and around the polaritonic gap are found.