Abstract Replicating the operation of biological neurons using electronic hardware is of significant interest for engineering and biomedical applications. Spiking neural network (SNN) models are especially suited as they exhibit temporal dynamics and local synaptic plasticity, closely mimicking biological neural function. To enable biological interaction, real-time response, and the ability to explore and deploy multiple neural models becomes also necessary. In this work, the Hardware Emulator of Evolving Neural Spiking Systems (HEENS), an efficient, fully digital architecture intended for real-time execution of SNNs, is reported. Based on Single Instruction Multiple Data (SIMD) computation, an array of simple but programmable processing elements is controlled by a sequencer dispatching common instructions. Local distributed memory avoids data bottlenecks and enables parallel parameter updates and interconnect reconfiguration. The address-encoded spikes are decoded by local associative memories, that can be modified on the fly, thus supporting evolvable networks. A synchronous ring topology based on fast point-to-point serial links enables multi-node systems with minimal latency and excellent scalability. A control node controls and configures the ring nodes, drives the system execution, and monitors the processed data. The hardware is supported by a user-friendly custom set of tools that performs a simple and fast compilation of neural/synaptic algorithms and network topology on a host computer. The results of field-programmable gate array (FPGA) implementation are reported. Multimodel real-time execution of proof-of-concept networks demonstrates the proposed architecture potential.

Abstract The increasing demand for faster, energy-efficient, and higher bandwidth semiconductor devices has pushed conventional Si-based scaling to its fundamental limits, including mobility degradation, short-channel effects, and high power consumption. To overcome these challenges, three-dimensional integration has emerged as a promising strategy, but wafer-based approaches like through-Si-via face critical limitations in stacking density, mechanical stress, and fabrication complexity. Two-dimensional materials provide a compelling alternative due to their atomically thin structure, superior electrical and mechanical properties, and ability to sustain performance at the atomic scale. Moreover, their van der Waals integration enables heterogeneous, high-density, and efficient assembly of functional layers. This review summarizes recent advances in the preparation and van der Waals integration of 2D materials, including growth, transfer, and direct integration. Their applications in intelligent computing that range from logic to sensor devices and their potential as next-generation electronics are discussed.

Abstract The human visual system can effectively sense optical information through the retina and process it at the visual cortex. Compared with conventional machine vision, it demonstrates superiority in terms of energy efficiency, adaptability, and accuracy. The retina-inspired machine vision systems can process information near or within the sensors at the front end, thereby compressing the raw sensory data and optimising the input to back-end processor for high-level computing tasks. In recent years, amid surge of AI technology, research in retinomorphic devices has achieved breakthrough in both academic and industrial settings. Herein, we present a comprehensive review of this emerging field based on several materials classes, such as halide perovskites, two-dimensional materials, organic materials and metal oxides. We discuss the steps taken towards achieving not only static pattern recognition, but also dynamic motion tracking and we identify the key challenges that need to be addressed by the community to push this technology forward.

Abstract In this work, we demonstrate the potential of HfO₂-based memristors as artificial synapses capable of reproducing biologically plausible spike-timing-dependent plasticity (STDP). W/HfO₂/Ti/TiN devices were fabricated and characterized, exhibiting reliable bipolar resistive switching, stable endurance, and reproducible resistance states across multiple cells and devices. The excitatory postsynaptic current (EPSC) response under sequential voltage pulses revealed gradual potentiation, depression, and saturation dynamics, closely resembling long-term potentiation, long-term depression, and synaptic consolidation in biological systems. Furthermore, the memristors successfully emulated higher-order learning rules, including triplet-STDP and frequency-dependent plasticity, while maintaining robust performance under biologically realistic noise conditions, exhibiting less than ±2% variation under voltage perturbations and ±2.5% under spike-timing jitter across 25 trials. A compact physical model captured the interplay between vacancy-driven filament dynamics and time-dependent weight modulation, yielding STDP curves consistent with experimental observations in neuroscience. These findings highlight HfO₂ memristors as promising candidates for neuromorphic computing, providing not only a faithful hardware realization of synaptic learning but also compatibility with large-scale, CMOS-integrated architectures for next-generation cognitive processors.

Abstract Physical systems exhibiting hysteresis are increasingly being used in neuromorphic and in-memory computing research. Generally, the resistance switching of devices with rate-independent hysteresis are being investigated for their use as trainable weights in neural networks, whereas the dynamics of devices showing rate-dependent hysteresis are being investigate for their potential as nodes, for example in reservoir computing systems. In our work we instead investigate the computing potential of a simple rate-independent hysteresis system. We show that by driving a system of only two linear branches with time-multiplexed inputs it is possible to generate nonlinear transforms and perform timeseries prediction tasks.

Abstract Tactile perception is fundamental to how humans interact with the world, underpinning both physical manipulation and emotional experiences. This complex sensory system relies on highly specialized mechanoreceptors embedded in the skin, which detect and relay signals through afferent neuronal fibers to the brain. Mechanoreceptors and afferent neurons perform local signal preprocessing, enabling transduction, filtering, adaptation, and sensory encoding before transmission to the central nervous system. While artificial tactile sensors inspired on the sophisticated functionalities of biological systems have been proposed, doing so in an energy efficient manner requires the integration of mechanical energy harvesters as transducers. Triboelectric nanogenerators have demonstrated significant potential as artificial mechanoreceptors, offering fully self-powered tactile sensing capabilities while simultaneously being able to supply energy to nearby low-power electronic components involved in perceptual processing. To bring significant progress in this technology, novel devices should emulate not only the different response of the various skin mechanoreceptors but also the first layer of tactile encoding processing accomplished at the skin level, while also minimizing energy consumption and environmental impact. Only few triboelectric (TE) effect driven tactile sensors have achieved the emulation of sophisticated adaptation features, such as slow and fast adaptation, typical of human tactile afferent neurons. Two approaches have been followed so far, which comprise the development of multifunctional TE-transducers capable of replicating more than one biological mechanoreceptor adaptive behavior, and fully integrated sensors embedding the TE-transducer and neuromorphic devices. Both approaches are still at their infancy, both in materials and device design, and still lack the high degree of integration, reproducibility and scalability that is required for their interface with standard electronics. Addressing such a pivotal challenge will improve neuroprosthesis integration, tactile robotics, human–machine interfaces, and the field of neuromorphic engineering for extreme edge processing.

Abstract The emerging field of neuromorphic computing for edge control applications poses the need to quantitatively estimate and limit the number of spiking neurons, to reduce network complexity and optimize the number of neurons per core and hence, the chip size, in an application-specific neuromorphic hardware. While rate-encoding for spiking neurons provides a robust way to encode signals with the same number of neurons as an ANN, it often lacks precision. To achieve the desired accuracy, a population of neurons is often needed to encode the complete range of input signals. However, using population encoding immensely increases the total number of neurons required for a particular application, thus increasing the power consumption and on-board resource utilization. A transition from two neurons to a population of neurons for the LQR control of a cartpole is shown in this work. The near-linear behavior of a Leaky-Integrate-and-Fire neuron can be exploited to achieve the Linear Quadratic Regulator (LQR) control of a cartpole system. This has been shown in simulation, followed by a demonstration on a single-neuron hardware, known as Lu.i. The improvement in control performance is then demonstrated by using a population of varying numbers of neurons for similar control in the Nengo Neural Engineering Framework, on CPU and on Intel’s Loihi neuromorphic chip. Finally, linear control is demonstrated for four multi-linked pendula on cart systems, using a population of neurons in Nengo, followed by an implementation of the same on Loihi. This study compares LQR control in the NEF using 7 control and 7 neuromorphic performance metrics, followed by a comparison with other conventional spiking and non-spiking controllers.

Abstract Progress in neuromorphic computing requires efficient implementation of standard computational problems, like adding numbers. Here we implement a variety of sequential and parallel binary adders in the Lava software framework, and deploy them to the neuromorphic chip Loihi 2. To the best of our knowledge, up to now, a neuromorphic implementation of such parallel adders has not been reported. We describe the time complexity, neuron and synaptic resources, as well as constraints on the bit width of the numbers that can be added with the current implementations. Further, we measure the time required for the addition operation on-chip. Importantly, we encounter trade-offs in terms of time complexity and required chip resources for the three considered adders. While sequential adders have linear time complexity O ( n ) and require a linearly increasing number of neurons and synapses with number of bits n, the parallel adders have constant time complexity O ( 1 ) and also require a linearly increasing number of neurons, but nonlinearly increasing synaptic resources (scaling with n 2 or n n ). This trade-off between compute time and chip resources may inform decisions in application development, and the implementations we provide may serve as a building block for further progress towards efficient neuromorphic algorithms.

Abstract The computational substrate of biological systems exhibits remarkable abilities to learn complex skills quickly and efficiently. Inspired by this, we implement model-based reinforcement learning using spiking neural networks directly on mixed-signal neuromorphic hardware. This approach combines energy-efficient electronic circuits with high sample efficiency through alternating online (‘awake’) and offline (‘dreaming’) learning phases. Our model features two networks: an agent network that learns from real and simulated experiences and a world model network that generates simulated experiences. We validate this by training the system to play Atari Pong. First, we establish a baseline using only real experiences. Then, by ‘dreaming’, the required real experiences decrease significantly. The network dynamics runs in real-time on the analog neuromorphic circuits, with only the readout layers implemented and trained on a computer-in-the-loop. We present results that demonstrate the robustness and potential of energy-efficient mixed-signal neuromorphic processors for real-world applications.