Advanced Computation Techniques for Complex AI Algorithms

Neuromorphic computing, inspired by the biological brain’s efficiency in processing information, has emerged as a revolutionary paradigm for next-generation edge artificial intelligence (AI). This research presents a comprehensive bio-inspired neuromorphic framework that leverages spiking neural networks (SNNs), memristor-based synaptic architectures, and event-driven processing to achieve unprecedented energy efficiency and real-time adaptability in edge computing environments. The proposed system introduces a dynamic spike encoding mechanism that optimizes neuronal activation based on input relevance, coupled with adaptive synaptic pruning to minimize redundant computations. Additionally, the integration of spike-timing-dependent plasticity (STDP) enables continuous self-learning, making the system highly effective for dynamic edge applications such as autonomous navigation, real-time healthcare monitoring, and industrial IoT anomaly detection. Experimental validation demonstrates that the proposed neuromorphic framework achieves a 60% reduction in power consumption, a 3× improvement in processing speed, and a 45% enhancement in model adaptability compared to conventional deep learning models deployed on edge platforms. Hardware efficiency is further amplified through memristor-based in-memory computing, eliminating the energy overhead associated with von Neumann architectures. Real-world evaluations across multiple edge scenarios—including neuromorphic vision processing with event-based cameras—confirm significant improvements in latency, robustness, and scalability. The findings underscore the transformative potential of neuromorphic computing in enabling sustainable, low-power AI for next-generation edge devices. Future research directions include hybrid neuromorphic-deep learning integration and quantum-inspired architectures to further enhance performance and scalability in ultra-low-power edge AI applications.

Efficiently solving combinatorial optimization problems (COPs) such as Max-Cut is challenging because the resources required increase exponentially with the problem size. This study proposes a hardware-friendly method for solving the Max-Cut problem by implementing a spiking neural network (SNN)-based Boltzmann machine (BM) in neuromorphic hardware systems. To implement the hardware-oriented version of the spiking Boltzmann machine (sBM), the stochastic dynamics of leaky integrate-and-fire (LIF) neurons with random walk noise are analyzed, and an innovative algorithm based on overlapping time windows is proposed. The simulation results demonstrate the effective convergence and high accuracy of the proposed method for large-scale Max-Cut problems. The proposed method is validated through successful hardware implementation on a 6-transistor/2-resistor (6T2R) neuromorphic chip with phase change memory (PCM) synapses. In addition, as an expansion of the algorithm, several annealing techniques and bias split methods are proposed to improve convergence, along with circuit design ideas for efficient evaluation of sampling convergence using cell arrays and spiking systems. Overall, the results of the proposed methods demonstrate the potential of energy-efficient and hardware-implementable approaches using SNNs to solve COPs. To the best of the author's knowledge, this is the first study to solve the Max-Cut problem using an SNN neuromorphic hardware chip.

Chapter 17 - Overview and coda: The future of AI

Hardware attacks on resource-constrained IoT devices are evolving rapidly. These threats have become a significant concern due to the increase of IoT devices used in applications such as human health, public transportation, autonomous vehicles, defense, and environmental monitoring. Recent studies show the potential of using deep learning to steal user data by monitoring hardware features and side-channel information. Additionally, machine learning (ML) approaches have recently been widely adopted in IoT applications. Advanced platforms demand novel circuits and architectures that can yield several orders of magnitude improvements in energy consumption in ML applications while maintaining consistent accuracy. Neuromorphic computing leveraging digital, mixed-signal, and analog processing has been shown to be a promising candidate due to energy, wire count, and area efficiency. Thus, an effective cutting-edge hardware approach for neuromorphic computing to perform rapid, energy-efficient, and secure supervised and unsupervised learning at the IoT edge is sought. Here we discuss the challenges and potential benefits of using neuromorphic computing modules for security at the IoT edge. The intersection of neuromorphic computing and hardware security serves many IoT domains in mission-critical and privacy-preserving applications.

Abstract: This chapter explores the design and development of photonic processors for ultra-fast data processing in high-performance computing (HPC) environments. By leveraging light instead of electricity for computation, photonic processors offer significant advantages in terms of speed, bandwidth, and energy efficiency. The chapter covers advancements in photonic materials, device engineering, and optical interconnects that enable scalable photonic systems for real-world applications, including AI, machine learning, and quantum computing. Key topics include the integration of photonic processors with quantum communication networks, the role of photonic multiplexing in parallel data processing, and innovations such as all-optical neurons for neuromorphic computing. The chapter also addresses current challenges, including phase sensitivity and the development of photonic quantum gates, offering solutions for scaling photonic computing to meet the demands of HPC and quantum networks. Keywords: photonic processors, high-performance computing, ultra-fast data processing, optical interconnects, photonic materials, quantum communication, AI, machine learning, photonic multiplexing, neuromorphic computing, phase sensitivity, quantum gates, scalable systems.

Multiple emerging computing technologies based on, e.g., graphene, spintronics, resistive RAM, quantum computing, and others are being developed to enhance the capabilities of logic devices and circuits. The rapid growth in these technologies is synchronized with the decline of Moore’s law, thus promises to herald the era of Beyond CMOS technologies with a significant improvement in energy efficiency, reliability, performance, and manufacturability. These devices enable very different computing paradigms, e.g., neuromorphic computing, non-Boolean computing, and in-memory computing, thus making these platforms an interesting playground for circuit and application-developers alike.

Monolithic 3D integration of neuron and synapse devices is considered a promising solution for energy-efficient and compact neuromorphic hardware. However, achieving optimal performance in both training and inference remains challenging as these processes require different synapse devices with reliable endurance and long retention. Here, we introduce a decoupling strategy to separate training and inference using monolithically integrated neuromorphic hardware with layer-by-layer fabrication. This 3D neuromorphic hardware includes neurons consisting of a single transistor (1T-neuron) in the first layer, long-term operational synapses composed of a single thin-film transistor with a SONOS structure (1TFT-synapses) in the second layer for inference, and durable synapses composed of a memristor (1M-synapses) in the third layer for training. A 1TFT-synapse, utilizing a charge-trap layer, exhibits long retention properties favorable for inference tasks. In contrast, a 1M-synapse, leveraging anion movement at the interface, demonstrates robust endurance for repetitive weight updates during training. With the proposed hybrid synapse architecture, frequent training can be performed using the 1M-synapses with robust endurance, while intermittent inference can be managed using the 1TFT-synapses with long-term retention. This decoupling of synaptic functions is advantageous for achieving a reliable spiking neural network (SNN) in neuromorphic computing.

Neuromorphic computing is an emerging field with the potential to offer performance and energy-efficiency gains over traditional machine learning approaches. Most neuromorphic hardware, however, has been designed with limited concerns to the problem of integrating it with other components in a heterogeneous System-on-Chip (SoC). Building on a state-of-the-art reconfigurable neuromorphic architecture, we present the design of a neuromorphic hardware accelerator equipped with a programmable interface that simplifies both the integration into an SoC and communication with the processor present on the SoC. To optimize the allocation of on-chip resources, we develop an optimizer to restructure existing neuromorphic models for a given hardware architecture, and perform design-space exploration to find highly efficient implementations. We conduct experiments with various FPGA-based prototypes of many-accelerator SoCs, where Linux-based applications running on a RISC-V processor invoke Pareto-optimal implementations of our accelerator alongside third-party accelerators. These experiments demonstrate that our neuromorphic hardware, which is up to 89× faster and 170× more energy efficient after applying our optimizer, can be used in synergy with other accelerators for different application purposes.

The cutting edge of computing is always changing because people are always looking for new ways to do things and combining cutting-edge tools. There are a number of natural trends that will affect the next generation of apps. Quantum computing is one of the most important trends. It is a new way of doing things that uses the rules of quantum physics to do calculations at speeds that are impossible with regular computers. Quantum computing could help solve hard issues in security, optimization, and modeling, which would lead to new science discoveries and technological progress.Neuromorphic computing, which is based on the structure of the human brain, is another new realm. The goal of this revolutionary way of computing is to create artificial neural networks that can learn, change, and process information in ways that are similar to how biological systems do it. There is a lot of promise for neuromorphic computing to improve machine learning and make AI apps more efficient and complex.The world of computing is also changing because artificial intelligence (AI) and edge computing are coming together. Edge computing brings computing power closer to the data source, which lowers delay and improves real-time processing. This is why it is so important for the growth of technologies like self-driving cars, smart cities, and the Internet of Things (IoT).Adding blockchain technology is another step forward that will make sure deals in open systems are safe and clear. Blockchain is used for more than just cryptocurrency. It has an impact on supply chain management, healthcare, and banking, among other areas. The quantum computing, neuromorphic computing, the combination of AI and edge computing, and the use of blockchain technology are all on the cutting edge of computing. Together, these trends bring computer science into a new era and lay the groundwork for groundbreaking uses that will shape the future of technology.

Exploring the trade-off between computational power and energy efficiency: An analysis of the evolution of quantum computing and its relation to classical computing

The process of using electronic circuits to replicate the neurobiological architectures seen in the nervous system is known as neuromorphic engineering, also referred to as neuromorphic computing. These technologies are essential for the future of computing, although most of the work in neuromorphic computing has been focused on hardware development. The execution speed, energy efficiency, accessibility and robustness against local failures are vital advantages of neuromorphic computing over conventional methods. Spiking neural networks are generated using neuromorphic computing. This chapter covers the basic ideas of neuromorphic engineering, neuromorphic computing, and its motivating factors and challenges. Deep learning models are frequently referred to as deep neural networks because deep learning techniques use neural network topologies. Deep learning techniques and their different architectures were also covered in this section. Furthermore, Emerging memory Devices for neuromorphic systems and neuromorphic circuits were illustrated.

Developing quantum systems which are robust against noise are of prime importance to the realisation of quantum technologies. Without fault tolerance, we will never be able to preserve delicate quantum states for macroscopic time scales, a necessary requirement in the construction of scalable quantum computer. Topologically ordered models offer very beautiful mechanisms for preserving quantum states. In such models, quantum information is encoded globally over a degenerate topological Hilbert space which offers a natural robustness against local environmental noise. Remarkably, topological order is present in certain realistic condensed-matter systems. This provides a platform for accessing topological order in a laboratory. Moreover, considering condensed-matter systems enables us to combine topological features with other physical effects to enhance their behaviour. In this Thesis we study the physics of topologically ordered lattice models with defects. We seek practical applications of lattice defects for the realisation of a fault-tolerant quantum computer. The first result we present shows that anyonic data of point-like lattice defects, called twists, can be found by measuring the entanglement of the ground state of the host system. The data we learn relates to the capacity of a twist defect to perform quantum computational tasks. The second result in this Thesis concerns the dynamics of the qudit toric code model with line-like defects coupled to a thermal bath. We show we can entropically inspire fragile glassy dynamics in the system. Such dynamics qualitatively improve the coherence times of quantum information encoded in the ground state of the lattice. A novel way of achieving fault-tolerant quantum computation is by producing and manipulating twist defects of topologically ordered systems. In many ways, this paradigm of topological quantum computation is analogous to quantum computation using anyons. The first area of study in this Thesis extends the analogy between anyons and twists. Specifically, we show that the anyonic data of twists in Kitaev’s toric code model can be extracted

Classic computing can take credit for technology ranging from mobile phones to supercomputers. But in recent years, a budding counterpart to these conventional devices has emerged: quantum computers. Whereas classic computing sometimes fails to solve complex calculations, such as factoring hundred-digit numbers, quantum computing holds the potential to easily tackle such problems. The field of quantum computing has attracted researchers, such as National Academy of Sciences member Christopher Monroe. An experimental atomic physicist at the University of Maryland and fellow of the Joint Quantum Institute and the Joint Center for Quantum Information and Computer Science in Maryland, Monroe uses lasers to exploit atomic particles to study complex computational problems. Monroe spoke to PNAS about how quantum computing might evolve. Christopher Monroe. > PNAS:Can you describe some of the basic mechanics of quantum computing? > Monroe:It all comes down to the superposition principle, in which a quantum system can exist in multiple states at the same time. Consider the fundamental unit of information: the bit. A bit is binary information that can be a 0 or a 1. A quantum bit, or qubit, can be in a superposition of both 0 and 1 as long as it’s isolated and unobserved. A single qubit is pretty trivial, but when you put many qubits together, there become exponentially many possibilities. > > Let’s consider 300 qubits. That’s an interesting number because the number of possible configurations, 2300, is more than the number of particles in the universe. You could never simulate what happens with 300 qubits on a classical machine; there’s not enough stuff in the universe to do it. The magic with quantum is the power of exponential growth: every time you add a qubit, you double the number of configurations. > > But to control these qubits and exploit this massive storage, we …

Neuromorphic computing with non-volatile memory (NVM) can significantly improve performance and lower energy consumption of machine learning tasks implemented using spikebased computations and bio-inspired learning algorithms. High voltages required to operate certain NVMs such as phase-change memory (PCM) can accelerate aging in a neuron's CMOS circuit, thereby reducing the lifetime of neuromorphic hardware. In this work, we evaluate the long-term, i.e., lifetime reliability impact of executing state-of-the-art machine learning tasks on a neuromorphic hardware, considering failure models such as negative bias temperature instability (NBTI) and time-dependent dielectric breakdown (TDDB). Based on such formulation, we show the reliability-performance trade-off obtained due to periodic relaxation of neuromorphic circuits, i.e., a stop-and-go style of neuromorphic computing.

Magnetic skyrmions are particle-like, nanometer-sized topological spin textures observed in several magnetic materials. They have emerged as an alternative to conventional spintronic memories and domain walls (DWs) and offer high storage density, more robust stability, low critical currents, and increased scalability. Recent advances have set the stage for their use in quantum computing, logic circuits, and neuromorphic computing. With the aid of electrical methods, it is possible to precisely create, manipulate, and destroy skyrmions in device-compatible materials. However, the maximum speed achievable by magnetic skyrmions and the reliable detection of data have been restricted by the skyrmion Hall effect (SkHE). Other issues include a low read margin and a lack of proper skyrmion motion control in nanowires. Most of these can be addressed by exploiting novel materials, such as antiferromagnets; employing specialized fabrication techniques; tuning driving current profiles; and circuit-level engineering. In this article, theoretical and experimental breakthroughs and challenges relevant to magnetic skyrmions and their applications in data storage, logic computing, and neuromorphic computing are highlighted.