Greetings, dear readers!

In our previous articles, we discussed classical processor architectures that laid the foundation for modern computers. However, technology does not stand still, and today we will look at a completely different approach to computing - neuromorphic architectures. These solutions, inspired by the principles of biological neural networks, open up new horizons for high-performance and energy-efficient systems.

Neuromorphic architectures trace their origins back to the mid-20th century, when researchers first turned their attention to how biological neural networks operate. In 1943, Warren McCulloch and Walter Pitts proposed a mathematical model of a neuron, demonstrating that the brain could be described using logical operations. This approach laid the groundwork for future research in the field of artificial intelligence. In 1958, Frank Rosenblatt introduced the perceptron—the first trainable model of an artificial neuron capable of recognizing patterns. Then, in the 1980s, Carver Mead of the California Institute of Technology laid the foundations of neuromorphic computing by coining the term “neuromorphic systems” and proposing the idea of creating hardware devices that mimic the operation of biological neural networks. His work was revolutionary, as it suggested moving away from the traditional digital approach toward the analog principles inherent to the human brain.

Around the same time in the USSR, a research community emerged dedicated to “neurocomputers” (hardware implementations of artificial neural networks using various analog electronic components). Significant achievements in developing such systems were made by scientists led by Dr. Alexander Ivanovich Galushkin, one of the pioneers of the backpropagation method widely used to train formal neural networks.

Throughout the 1990s, neuromorphic systems remained largely experimental, yet important theoretical developments during that period paved the way for current achievements. In the early 21st century, new neural network models such as Spiking Neural Networks (SNN) began to appear, offering a more accurate reflection of how biological systems function. Riding this wave, commercial neuromorphic chips such as IBM TrueNorth and Intel Loihi were created, and a large-scale SpiNNaker project was launched at the University of Manchester. These and other initiatives demonstrated the effectiveness of neuromorphic architectures in data processing tasks. So what makes them special? Let’s find out.

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Fig1. Comparison of the von Neumann architecture with the neuromorphic architecture

Unlike traditional systems in which the arithmetic - logic unit processes information sequentially, neuromorphic systems leverage the parallel operation of numerous “neurons.” They exchange brief impulses (“spikes”), providing both high responsiveness and impressive scalability. These features open up new opportunities for building high-performance and energy-efficient systems modeled on the principles of living neural networks. To better understand how these systems replicate the properties of biological neural networks, let’s examine their key characteristics.

Key Characteristics of Neuromorphic Architectures:

  1. Massive Parallelism.
    The human brain contains about 86 billion neurons, each with between 1,000 and 10,000 connections - synapses. For an electronic system to mimic biocomparable neural networks, it needs a high degree of massive parallelism. This means millions or even billions of computing elements can operate simultaneously, ensuring high performance and low power consumption.

  2. In-Memory Computation.
    In living organisms, neurons and synapses both store information and perform computations. Neuromorphic systems should also have this capability - storing data and performing addition and multiplication operations directly in the same location. This setup helps minimize problems associated with the “memory wall” in traditional digital architectures, where delays in transferring data between the processor and memory significantly reduce performance.

  3. Sparse Computation.
    In biological systems, only those neurons required for a specific task are active at any given time. Likewise, neuromorphic systems should efficiently use only a fraction of the available computing resources, which significantly reduces power consumption compared to digital architectures that engage all computing elements regardless of necessity.

  4. Energy Efficiency.
    The brain operates at an extremely low energy level (1–100 femtojoules per synaptic event). Neuromorphic architectures must deliver high performance with minimal energy usage to be suitable for mobile devices, embedded systems, and autonomous devices with limited resources.

  5. Support for Spiking Neural Networks.
    Spiking is a key way biological neural networks encode information. For handling event-driven data (e.g., video streams from event-based cameras), neuromorphic systems need to operate efficiently in real time, processing data at the event level.

  6. Plasticity and Self-Learning.
    In living systems, neurons alter their connections in response to stimuli, adapting to new conditions. Neuromorphic systems must have built-in plasticity, allowing them to change “synaptic weights” during operation in order to exhibit adaptive behavior without manual reconfiguration.

Advantages of Neuromorphic Architectures:

  1. Real-Time On-Device Computation.
    Neuromorphic systems can process data locally, right on the device, which reduces latency and opens up new application areas, including neuroprosthetics, autonomous systems, and robotics.

  2. Self-Learning.
    Neuromorphic architectures adapt to changes in the environment thanks to mechanisms for updating connections between “neurons.” This makes them especially useful for tasks involving unpredictable or dynamic conditions.

  3. High Energy Efficiency.
    The energy efficiency of neuromorphic chips allows them to be used in resource-constrained devices such as mobile gadgets, Internet of Things sensors, and unmanned systems.

  4. Versatility and Scalability.
    Despite technical challenges, neuromorphic systems offer flexibility of application and the potential for integration in various fields, from modeling brain function to processing large volumes of data.

Despite these clear advantages and the enormous potential of neuromorphic architectures, their development and adoption face a number of challenges, driven by both technological constraints and the relative immaturity of current solutions. Let’s look at the main issues holding back the evolution of neuromorphic systems.

Challenges:

  1. Complex Development.
    Programming neuromorphic systems requires specialized approaches, knowledge of neurobiology, and unique toolchains, raising the entry barrier for developers.

  2. High Hardware Costs.
    The design and production of neuromorphic chips remain expensive, particularly when scaling such systems for complex applications.

  3. Lack of Standards.
    The absence of unified standards for neuromorphic architectures complicates the development of compatible solutions and their integration into existing systems.

  4. Training Algorithm Constraints.
    Traditional methods like backpropagation do not always apply effectively to spiking neural networks, creating additional hurdles in development.

  5. Scalability.
    Although neuromorphic architectures demonstrate high performance in specialized tasks, scaling them to handle large datasets remains a significant technical challenge.

Growing interest from leading companies and research institutes suggests these obstacles will be gradually overcome. Major technology corporations such as IBM, Intel, and Qualcomm - and research institutions worldwide - are investing substantial resources in the research and development of next-generation neuromorphic chips. As the hardware base matures and new algorithms and standards emerge, these chips are likely to assume an important role in everyday devices. This will bring us closer to the operating principles of the human brain and elevate intelligent computing systems to a fundamentally new level.

It is worth noting that research and development in the field of neuromorphic technologies are actively pursued not only abroad but also in Russia. Scientists at the IPPI RAS, MIPT, NNSU, LETI, SFedU, Kurchatov Institute, and other organizations work on spiking neural network modeling, creating energy-efficient chips, and studying synaptic plasticity. Their developments are used in robotics (for real-time data processing and control), as well as in AI and autonomous technologies. The efforts of Rosatom and Kaspersky Lab in the area of neuromorphic systems underscore the strategic importance of such solutions for industry, energy, and information security. Their research helps solve critical challenges, increasing reliability, performance, and adaptability of AI-based systems.

Our team is deeply interested in neuromorphic systems and is actively engaged in their advancement. We are currently creating a specialized Electronic Design Automation (EDA) system that will provide developers with a convenient and powerful tool for modeling, designing, and testing neuromorphic devices. Our system combines cutting-edge algorithms, an intuitive interface, and modern approaches, forming a unified software environment for effective development. We hope our achievements will help accelerate progress in this field and bring artificial intelligence closer to the capabilities of the human brain. You can learn more about our product on the website.

Dear readers, if you are interested in the topic of neuromorphic architectures, please share in the comments which neuromorphic systems you are familiar with and which ones you would like to learn more about. In the following articles, we will take a closer look at TrueNorth, Loihi, SpiNNaker, ReckOn, Tianjic, their design, and key features. Stay tuned - there’s much more to come!

Thank you for being with us!
Sincerely, the MemriLab team