Neuromorphic Computing for Artificial Intelligence PDF

**A Study on Neuromorphic Computing for Artificial Intelligence** **Hadeel A. Abd EL\_AAl^1^** *^1^Faculty of Computer and Information, Fayoum University, Cairo, Fayoum.* **Abstract** The advancement of artificial intelligence (AI) requires significant storage and processing capabilities, facing...

**A Study on Neuromorphic Computing for Artificial Intelligence** **Hadeel A. Abd EL\_AAl^1^** *^1^Faculty of Computer and Information, Fayoum University, Cairo, Fayoum.* **Abstract** The advancement of artificial intelligence (AI) requires significant storage and processing capabilities, facing challenges like high power consumption and hardware overhead. The human brain, with its approximately one hundred billion neurons operating on just 20 watts, exemplifies energy efficiency and inspires the development of neuromorphic computing. This approach aims to create a framework for neuroscience to explore brain learning processes and apply these insights to cognitive computing. Neuromorphic computing offers advantages over traditional methods, including enhanced energy efficiency, faster processing, resilience to failures, and learning capabilities. This article reviews recent literature in this evolving field, covering fundamental concepts, hardware advancements, potential applications, and limitations. The review includes an overview of neuromorphic computation, circuit architecture, key hardware components, promising advancements, proposed applications, and challenges hindering widespread adoption. **Keywords:** neuromorphic computing, memristors, spintronics, in memory computing, biocomputing. 1. **Introduction:** Neuromorphic engineering represents an emerging paradigm in information processing that aims to emulate the robust, distributed, asynchronous, and adaptive characteristics of biological intelligence within complementary metal-oxide semiconductor (CMOS), very large-scale integration electronics and other innovative technologies.**\[1\]** This field is inherently interdisciplinary, drawing inspiration from biology, physics, mathematics, computer science, and electronic engineering to create artificial neural systems, including vision systems, head-eye systems, auditory processors, and autonomous robots **\[2\].** The goal is to replicate certain features of biological neural networks as either analog or digital representations within electronic circuits.**\[3\]** Consequently, neuromorphic systems offer several advantages over traditional Von Neumann architectures, including: parallel processing, integrated processing and memory, inherent scalability, event-driven computations characterized by sparsity, stochasticity---which incorporates elements of randomness akin to neuronal firing to accommodate noise) **\[4\],** ---and in-memory computing, where each neuron possesses its own memory or stored state, thereby eliminating the necessity for transferring intermediate data or competing for memory access. **\[5\]** Fig. 1 **Fig (1):** Comparison of the von Neumann architecture with the neuromorphic architecture.\[4\] **2. Neuromorphic Computation: General Architecture** A neuromorphic computer or chip refers to any device that employs physical artificial neurons for computational tasks. Recently, the term \"neuromorphic\" has been applied to various systems, including analog, digital and mixed-mode i.e, Very Large Scale Integration (VLSI), and software frameworks that simulate neural system models for functions such as perception, motor control, or multisensory integration. **\[2\]** In today\'s landscape of extensive data, the translation of brain-like functionalities into hardware systems is crucial for achieving artificial intelligence on a chip in a manner that is both power-efficient and scalable. The neuromorphic computing paradigm provides intelligent systems capable of efficiently training on unstructured big data through the use of spiking or oscillatory neural networks. Spiking neural networks (SNN) employ leaky integrate-and-fire neurons along with spike-time-dependent synaptic plasticity to facilitate training and inference on spatiotemporal data, while oscillatory neural network (ONN) models represent neurons as coupled oscillators to address combinatorial and computationally challenging problems. **\[6\]** Traditionally, the most biologically plausible implementations of neuromorphic hardware are grounded in SNN, which constitutes a third generation of artificial neural networks (ANN). These networks emulate biological neurons and their associated synaptic weights in hardware, thereby closely mirroring the functionality of the human brain. This makes them particularly well-suited for machine learning (ML) applications in the current era of big data. **\[7\]** The components of a neuromorphic computing system (NCS) encompass the computational unit (neural model), the information storage unit (synaptic model), the communication unit (dendrites and axons), and the learning mechanism (weights update) **\[5\].** At the hardware level, these systems can be constructed using digital, analog, and mixed-signal circuits.**\[8\]** **Figure (2).** Neuromorphic architecture characterization diagram. **2.1. Neuromorphic Computation: Digital Circuit Implementation:** The digital realization of neuromorphic architecture can be achieved through the use of FPGAs, ASICs, or a heterogeneous system that integrates CPUs and GPUs. **2.1.1. Field-Programmable Gate Arrays (FPGAs):** (FPGAs) offer numerous benefits for neuromorphic computing, including adaptability, high performance, reconfigurability, and remarkable stability. Furthermore, they are capable of implementing Spiking Neural Networks (SNNs) due to their parallel processing capabilities and adequate local memory size for weight storage. Recent developments in FPGA-based neuromorphic systems have employed random access memory (RAM) to enhance memory access latency. **\[5\]** **2.1.2. Application-Specific Integrated Circuits (ASICs):** ASICs offers advantages such as low power consumption and high-density local memory, which are beneficial for the advancement of neuromorphic systems. Contemporary ASICs incorporate flash memory, which boasts a long retention period exceeding ten years. This flash memory features a three-terminal configuration, operates on a charge-based principle, and is classified as non-volatile.\ Nevertheless, these implementations tend to be less adaptable and incurs higher manufacturing costs in comparison to Field-Programmable Gate Arrays (FPGAs). Additionally, ASICs are constrained to particular neuron models and algorithms. **\[5\]** **2.1.3. Heterogeneous system architecture:** Integrates both central processing units (CPUs) and graphics processing units (GPUs) allows for enhanced programming flexibility through the use of CPUs, while also facilitating parallel processing and accelerated computing via GPUs. However, these systems face challenges in scalability due to their significant energy requirements. **\[5\]** **2.2. Neuromorphic Computation: Implementation of Analogue Circuits:** Conversely, analogue circuits designed for neuromorphic computing can be constructed using memristors and CMOS technology. The hardware realization of these circuits can be achieved through various components, including oxide-based memristors, resistive RAM, spintronic memories, threshold switches, and transistors. The intricacies of this implementation are closely related to the principles of Reservoir Computation. **\[2\]**,**\[5\]** **2.3. Neuromorphic Computation: Comparison of Digital and Analogue Circuit Implementations:** In digital implementations, data transfer between an arithmetic logic unit (ALU) and memory cells is necessary, which complicates large-scale deployment. Nevertheless, digital systems can accommodate nearly any learning algorithm, offering greater customization and flexibility. In contrast, analogue devices utilize spiking neural networks (SNN) to represent information through spikes. This approach is generally more cost-effective than digital designs and facilitates in-memory computing. **\[5\]** Additionally, analogue systems exhibit a high degree of resilience to faults and noise, although they lack the same level of flexibility.**\[8\]** Reference **\[5\]** highlights two significant challenges in analogue circuits: the testing and debugging of large-scale analogue circuits and the construction of complex analogue neural computing systems to achieve substantial throughput. Despite extensive research aimed at identifying an effective hardware platform for the efficient implementation of spiking neural network-based neuromorphic computing, a suitable solution remains elusive. Although neuromorphic computing could theoretically be realized with individual devices that exhibit both neuronal and synaptic characteristics, the absence of practical implementations that do not require supplementary interface circuitry--- which compromises area and energy efficiency---renders digital circuit-based approaches the most viable option. Indeed, some of the most successful commercial neuromorphic circuits to date, such as Intel's Loihi5 and IBM's TrueNorth, are based on digital circuit methodologies, utilizing the widely adopted complementary metal-oxide-semiconductor (CMOS) technology.**\[7\]** **2.4. Neuromorphic Computation: Mixed Circuits:** A hybrid design strategy that integrates the benefits of both analog and digital implementations can address numerous constraints. Digital information, represented as digital spikes, can be employed in analog neuromorphic systems, thereby enhancing the retention time of synaptic weights and improving system reliability. **\[5\]** The mixed-signal implementation of neuromorphic computing systems (NCS) leverages the robustness and high precision of digital signals alongside the low power consumption of analog signals. A particularly promising development in mixed-signal NCS is the advent of memristors, which utilize their analog resistance for the storage of synaptic weights. **\[8\]** **3. Neuromorphic Analog Hardware:** **3.1. Neuromorphic Analog Hardware: Transistors** Transistor-based devices are regarded as effective for simulating synaptic functions in neuromorphic computing due to their synergistic control over changes in synaptic weight. **\[9\]** A variety of low-dimensional inorganic materials, including silicon nanomembranes, carbon nanotubes, nanoscale metal oxides, two-dimensional materials, silicon nanomaterials, perovskite materials, and ferroelectric materials, are utilized in the fabrication of transistor-based synaptic devices. These materials exhibit excellent physical properties and serve as channel layers and active components. **\[9\]** The different operational mechanisms of synaptic transistor devices include: 1\. Carrier Capture and Release. 2. Ionization and Neutralization of Oxygen Vacancies. 3. Ion-Gated Synaptic Transistors. 4. Ferroelectric Polarization. **\[9\]** **3.1.1. Complementary Metal Oxide Semiconductor (CMOS) Transistors:** Complementary metal oxide semiconductor (CMOS) transistors have been effectively utilized to create neurons and synapses within neuromorphic architectures. Furthermore, they are extensively employed in large-scale spiking neural networks (SNNs). **\[5\]** **3.1.2. Tunneling Field-Effect Transistors** (**TFETs) Transistors:** The constraints related to performance and energy efficiency in digital NM circuits constructed with CMOS can be addressed through the use of tunneling field-effect transistors (TFETs), which feature low OFF-state current and a subthreshold swing (SS) of less than 60 mV/decade. These characteristics render TFETs particularly promising for low-power circuit implementations. Additionally, the low operational frequency of NM circuits (approximately MHz) combined with their minimal activity factors---due to the infrequent firing of neurons---further enhances the appeal of TFETs for these applications. Notably, TFETs designed with atomically thin two-dimensional (2D) materials, which exhibit pristine interfaces and minimized band-tails, provide exceptional electrostatic performance and are relatively free of defects, resulting in sharp turn-on characteristics. **\[7\]** **3.1.3. Carbon Nanotube Field-Effect Transistors (CNTFETs):** Neuromorphic circuits are beginning to emerge that can function with greater speed and efficiency, thanks to the high-performance and energy-efficient characteristics of carbon nanotube field-effect transistors (CNTFETs). **\[10\]** While these transistor-based synaptic devices have made significant strides in replicating synaptic functions, their integration into neuromorphic computing remains in the nascent stages. **\[9\]** **3.2. Neuromorphic Analogue Hardware: Memristors:** In contrast to synapses constructed from multiple transistors, a single memristor can effectively replicate the function of a synapse while occupying less space on a chip and consuming less power. The two essential synaptic features that memristive devices must replicate are synaptic plasticity and efficacy. Numerous memristors hold the potential for application in neuromorphic computing or to enhance the digital computation based on the von Neumann architecture. **\[12\]** In the realm of neuromorphic computing, memristors are typically utilized by applying spikes to the electrode (presynaptic terminal), which initiates the release and transmission of \"neurotransmitters\" through the dielectric or semiconductor layer (synaptic cleft), leading to modulation of conductance (post-synaptic neuron potential). The extent of the conductance alteration is regarded as a synaptic weight. **\[12\]** Furthermore, synaptic characteristics such as long-term potentiation (LTP), short-term plasticity (STP), spike time-dependent plasticity (STDP), and spike-rate-dependent plasticity (SRDP) are influenced by the spikes at both presynaptic and postsynaptic neurons. Memristive devices can demonstrate synaptic plasticity by adjusting their conductance in response to appropriately applied electrical pulses. **\[12\],\[13\]** Memristors function as nonvolatile memory and facilitate in-memory computation, thereby addressing the von Neumann bottleneck by transitioning from a traditional computation-centric model to a data-centric approach that directly utilizes memory for data processing. **\[13\]** The justification for co-locating computing and memory arises from the fact that machine learning operations demand significant computational resources, yet the individual tasks are often straightforward. Specifically, these operations involve a substantial number of simple calculations, such as matrix multiplications. The primary constraint is not the speed of the processor, but rather the time and energy consumed in transferring data between memory and processing units, particularly under heavy workloads and in AI applications. **\[14\]** This issue is referred to as the von Neumann bottleneck, named after the von Neumann architecture that has been foundational in chip design since the inception of microchip technology. By utilizing in-memory computing, substantial reductions in energy consumption and latency can be achieved by eliminating the need for this data transfer in processes that are data-intensive, such as AI training and inference.**\[14\]** Memristors exhibit rapid operational speeds, minimal energy usage, and compact dimensions. They operate through a switching mechanism that utilizes programming pulses to transition between states. These devices can be categorized into non-volatile and volatile types; the non-volatile variant is suitable for in-memory computing applications, while the volatile type is commonly employed in synapse emulators, selectors, hardware security, and artificial neurons. **\[5\]** The distinctive characteristic of memristors is their ability to modify their resistance based on the past patterns of applied voltage or current. This characteristic enables the neural networks to retain previous states, rendering them well-suited for modeling synaptic behaviour. Memristors are also energy-efficient and capable of functioning at low temperatures. **\[14\]** These chips are characterized by a multitude of interconnected neurons, which facilitate parallel processing capabilities. They employ analog computation and event-driven processing, making them ideal for real-time applications such as robotics. Their adaptability is enhanced by mechanisms like spike-timing-dependent plasticity (STDP) learning **\[15\]**, which supports cognitive processing, extensive data analysis, reservoir computing, neuromorphic computing, and edge computing. **\[13\]** Most of the memristors reported to exhibit neuronal and synaptic characteristics are primarily based on first-order memristor models. However, the lack of internal dynamics necessitates the engineering of spike shapes and their overlaps to effectively replicate biological functions. **\[12\]** In contrast, recent research has revealed that the conduction mechanisms in memristors are affected by multiple internal state variables, categorizing them as higher-order memristors. These devices demonstrate intricate dynamical behaviors that are essential for encoding temporal information. Further advancements are required to enhance higher-order memristor devices and to create innovative architectures for future bio-realistic neuromorphic computing. **\[12\]** Resistive Access Memory (ReRAM) is a two-terminal nanodevice that utilizes resistive switching to deliver high-density, rapid, and energy-efficient memory solutions. **\[10\]** This technology holds significant promise as it facilitates highly parallel, ultra-low-power in-memory computing for artificial intelligence algorithms. Its structural simplicity allows for easy integration into systems while maintaining low power consumption. **\[5\]**, **\[10\]** **3.3. Neuromorphic Analogue Hardware: Spintronics:** Spintronic devices represent a class of low-power, high-speed solutions for data processing and storage, utilizing the spin of electrons rather than their charge. **\[10\]** Spintronic nanodevices, which exploit both the magnetic and electrical properties of electrons, can increase the energy efficiency and decrease the area of these circuits, and magnetic tunnel junctions are of particular interest as neuromorphic computing elements because they are compatible with standard integrated circuits and can support multiple functionalities.  \[16\] The fields of Nanomagnetism and spintronics exhibit numerous inherent characteristics that hold significant potential for neuromorphic computing. These characteristics encompass non-linearity, phase transitions, collective behavior, and non-volatility, which are advantageous for compute-in-memory applications. **\[16\]** **4. Neuromorphic Computing: Advanced Developments:** - Nanoscale memristors and synaptic transistors are attracting significant attention for the development of neuromorphic architectures and circuits, serving as alternatives to traditional CMOS circuits. In 2008, Christophe Novembre and colleagues reported the successful creation of a NOMFET (Nanoparticle Organic Memory FET) utilizing pentacene/Au nanoparticles, which demonstrated a charge retention time of 4500 seconds. **\[8\]** - The potential for extreme density in resistive switching matrices is enhanced by recent advancements in nanofabrication techniques, such as extreme ultraviolet lithography (EUV), which has already achieved half-pitch lines of less than 10 nm. **\[11\]** - Phase-change memory (PCM) represents a nonvolatile, high-density memory technology that leverages material phase transitions to store data, akin to the function of synapses. **\[10\]** - To enhance processing capabilities and connectivity, 3D integration incorporates multiple layers of circuitry, mimicking the complex network structure of the brain **\[10\].** - Redox memristors are noted for their exceptionally low energy consumption during switching, approximately 10 fJ, with switching times recorded as brief as 85 ps for nitride materials. **\[11\]** - Neuromorphic photonics is an advanced, interdisciplinary field that merges artificial intelligence (AI), photonics, and neuroscience. This innovative approach utilizes light-driven neurons and optical synapses to closely replicate the complex functions of human brain cells. By leveraging the speed of light and emulating the sophisticated neural networks of the human brain, neuromorphic photonics holds the promise of opening new avenues in high-performance computing, significantly enhancing capabilities in areas such as pattern recognition, data processing, and complex problem-solving.**\[17\]** - Biocomputing, a specialized branch of synthetic biology, employs biomolecular components as the foundational hardware for executing computations defined by humans. DNA computing, a subset of biocomputing, utilizes various biochemical and biophysical reactions involving DNA and enzymes to perform calculations, primarily based on the double-helix structure and the principles of complementary base pairing found in DNA molecules. **\[18\]** - Biomaterials, which are substances designed to interact with biological systems in a functional and compatible manner, are integral to the development of neuromorphic devices. These materials aim to replicate the synaptic connections found in the brain, enabling ultra-flexible artificial synaptic devices to perform functions such as learning, memory retention, and information processing. **\[19\]** Inkjet printing, self-assembly methods, and [electrochemical deposition](https://www.sciencedirect.com/topics/materials-science/electrodeposition) are only a few of the techniques to enable accurate biomaterial patterning and deposition, enabling the construction of complex neural networks on neuromorphic circuits. \[20\]  **Fig. 3: **Overview of memristors with biomaterials for bio realistic features.**\[14\]** - A new generation of semiconductor synthetic biology (SemiSynBio) technologies has been introduced, which combines techniques such as DNA computing and neuromorphic computing. This integration takes advantage of the inherent energy efficiency found in biological systems alongside the advanced computational capabilities of neuromorphic architectures, resulting in the development of powerful and energy-efficient computing technologies. **\[18\]** - Large-Scale Neuromorphic Arrays: Numerous significant designs of neuromorphic processors have been developed using analog, digital, and mixed-mode VLSI technology. The current leading large-scale neural arrays utilizing VLSI technology include Neurogrid (Stanford University), TrueNorth, SpiNNaker (University of Manchester), BrainScales (University of Heidelberg), and Loihi (Intel). **\[21\]** **5. Neuromorphic Computation: Applications:** Currently, there is no commercially available neuromorphic computing technology; however, Schuman et al. (2022) anticipate two significant domains for artificial intelligence applications. Firstly, neuromorphic computers have the potential to enhance AI functionalities on personal computing devices, including smartphones, laptops, and desktops, while also extending battery life. **\[21\]** Secondly, the low power consumption characteristic of neuromorphic hardware is particularly advantageous for edge computing applications. Edge computing involves processing and analyzing data near its source rather than relying on remote cloud services. This localized processing not only bolsters data security and privacy by minimizing network traffic but also optimizes efficiency. In the context of the Internet of Things (IoT), neuromorphic chips facilitate on-site data processing, thereby decreasing the necessity for data transmission to central servers, which conserves bandwidth and mitigates latency. **\[21\]** These capabilities are pertinent across various application domains, including autonomous systems such as vehicles and drones, remote sensors, wearable technology, prosthetics, smart homes, and robotics. They enhance sensory processing and movement control, enabling tasks that require autonomous decision-making and allowing robots to better interpret and engage with their surroundings. **\[21\]** Here are several examples of promising applications for neuromorphic computing, which may encompass, but are not limited to: **1.** Autonomous Vehicles: Neuromorphic systems possess the capability to process intricate sensory data at an accelerated pace, thereby enabling autonomous vehicles to make real-time navigation choices. The local data processing capability ensures that decisions are executed promptly, a crucial factor for ensuring safety in autonomous driving.**\[22\]** **2.** Smart Cameras: When enhanced with neuromorphic computing, smart cameras can conduct real-time image processing for various applications, including surveillance, traffic management, and crowd monitoring. The efficiency of neuromorphic chips allows these devices to function with reduced power consumption, thereby prolonging their operational lifespan in the field. **\[22\]** **3.** Voice-Assisted Technologies: Neuromorphic chips can significantly improve voice recognition systems, enhancing their performance in noisy environments. This advancement increases the reliability of voice-assisted devices in practical situations.**\[22\]** **4.** Aerospace and Defense: Neuromorphic computing provides a notable advantage in terms of speed and efficiency for applications that necessitate the rapid processing of large volumes of data, such as satellite image analysis and automated threat detection. **\[23\]** **5.** Robotics: The interdependence between soft biomaterials and the domain of electrical chip engineering is posing a challenge to the conventional notion of computing as a rigid and binary operation. On the other hand, it possesses capabilities beyond mathematical calculations, enables robots to adapt to their environment. These artificial synapses, which are influenced by biological systems. **\[20\]** **6.** Medical Field: Neuromorphic computing can enhance drug discovery processes and improve clinical trials by accurately simulating biological systems, thereby facilitating the development of targeted treatments. The collaboration between neuromorphic computing and neuroscience holds the potential to transform the understanding of the complexities of the human brain and address various neurological challenges, among other promising applications in the medical sector. **\[24\]**, **\[25\]** as illustrated in fig.4. Neuromorphic circuits represent promising options for creating interfaces that facilitate the exchange of information between biological brains and artificial computing systems. These interfaces can be categorized into three types: reading interfaces, which capture neural activity and decode its informational content---such as interpreting motor signals to control prosthetic devices; writing interfaces, which introduce information into the nervous system by stimulating neural tissue to generate meaningful neural activity---such as circuits that process tactile or visual data from artificial sensors and subsequently deliver it to damaged neural tissue to restore sensory capabilities; and closed-loop interfaces, which integrate both reading and writing functionalities. **\[1\]** There has been an increasing trend within the optical community to integrate machine learning and data science techniques into photonics research, resulting in successful implementations in areas such as optical microscopy, ultrafast optics, and optical communication. Optoelectronic devices facilitate high-speed, low-latency communication by processing and transmitting data through light. As the Internet expands, cloud computing serves as the foundation for these interconnected systems, enabling automated decision-making, smart home technologies, and real-time data transmission. These advancements represent some of the emerging and evolving technologies linked to neuromorphic computing. **\[10\]** The ability of soft biomaterials to replicate the synaptic functions of the brain opens up new possibilities for the development of efficient and intelligent computing systems. **\[21\]** Figure 1. Refer to the following caption and surrounding text. **Figure 4.** Neuromorphic computing has many applications and inspirations to-and-from medicine. Besides interfaces, neuromorphic detection algorithms also assist in diagnostic care (bottom-right). **\[25\]** **Device** **Description** **Applications** -------------------------------------- ------------------------------------------------------------------------------------- ---------------------------------------------------------------------------------------- Neuralink Develops high-bandwidth brain-computer interface (BCI) technology Motor function restoration, cognitive enhancement, treatment of neurological disorders Paradromics Specializes in high-channel count brain-machine interface (BMI) systems Assistive technologies, prosthetics, brain-computer interfaces Newronika Focuses on closed-loop stimulation systems for treating neurological disorders Epilepsy, movement disorders, chronic pain treatment Bioinduction Specializes in neurostimulation devices for treating neurological conditions Epilepsy, chronic pain, movement disorders treatment Medtronic Summit RC + S, Percept PC Neurostimulator devices with advanced features for managing neurological conditions Parkinson's disease, epilepsy, essential tremor management NeuroPace RNS System Implantable neurostimulation device for treating intractable epilepsy Treatment of epilepsy in non-responsive patients Abbott Infinity DBS System Neurostimulation device for managing movement disorders Parkinson's disease, essential tremor management Boston Scientific Vercise DBS System Neurostimulation device for managing movement disorders Parkinson's disease, essential tremor management Soterix Medical HD-tES System Non-invasive neuromodulation device for psychiatric and neurological disorders Depression, pain management, cognitive enhancement **Table 1**. Overview of commercial neurostimulation devices for neurological disorders.\[26\] **6. Neuromorphic Computation: Challenges:** An optimal neuromorphic platform would leverage the properties of neuromorphic computing in a cohesive manner. Such a system would incorporate numerous layers of resistive switching matrices integrated with conventional digital circuitry, thereby achieving high performance while maintaining low manufacturing costs. However, various performance and manufacturability challenges may hinder widespread industry adoption.**\[11\]** A significant barrier to the advancement of algorithms and applications for neuromorphic computers is the absence of easily accessible and user-friendly software and hardware systems for the broader computational and computer science communities. **\[27\]** Researchers have been endeavoring to replicate the diverse characteristics of biological neurons using either complementary metal-oxide-semiconductor (CMOS) technology or emerging nonvolatile memory (NVM) devices, including spintronic memory, resistive switching memory, phase change memory, and ferroelectric memory. Nevertheless, many of these methods, particularly those based on traditional CMOS technology for neuron circuits, necessitate multiple capacitors and numerous transistors, resulting in substantial power consumption and spatial requirements to emulate the intricate behaviors of biological neurons. **\[28\]** The advancement in the deployment of neural networks utilizing memristors as synaptic elements is noteworthy. Nonetheless, numerous challenges remain to be addressed at the material, device, and system levels to concurrently attain high accuracy, minimal variability, rapid processing, energy efficiency, compact size, affordability, and robust reliability. This necessitates collaborative research initiatives across these three interrelated domains: devices, circuits, and systems. **\[29\]** Various neuromorphic implementations exist; however, the availability of each type is limited, and they are generally accessible only through restricted cloud platforms to the wider community. **\[27\]** While simulators such as NEST, Brian, and Nengo are accessible, they are often designed for specific applications. For instance, NEST is primarily focused on computational neuroscience tasks, while Nengo is structured around the Neural Engineering Framework. As these software tools cater to particular communities and use cases, their overall usability and accessibility are constrained beyond those specific groups. **\[27\]** Reference **\[11\]** addresses the difficulties associated with essential metrics and suggests a strategic plan for achieving competitive memristive-based neuromorphic processing systems.  **Fig 5**. Roadmap for manufacturing challenges and possible approaches to accelerate progress.**\[11\]** **Conclusion** In summary, neuromorphic hardware technology is good to go and exhibits considerable promise as it holds the capacity to bring about transformative advancements in artificial intelligence, materials science, and diverse industrial sectors. Although the understanding of how to make best use of the hardware still lags behind, significant progress is being made. The collaboration of diverse teams is essential in order to fully leverage their potential and effectively address various difficulties. **References** \[1\] Stefano Panzeri et al. 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