This budget is for development of energy-efficient neuromorphic AI systems models for real-time decision support. Required PC components will enable the simulating and optimizing Spiking Neural Networks -SNNs, and ensure reliable results. Precision measurement equipment (oscilloscope and function generator) enables accurate testing and validation of prototypes. Edge AI devices (like Raspberry Pi 5) and NPU boards allow practical evaluation of SNN models in low-power decentralized environments. Prototyping materials such as PCBs, sensors, and analog/digital components are crucial for building and refining hardware. Operational costs cover software licenses, utilities, and cooling to sustain experiments. Finally, shipping and import fees ensure access to specialized tools unavailable locally. These resources will enable a systematic approach to simulation, testing, and prototyping, validating the energy efficiency and adaptability of SNN-based systems for critical real-time applications.

Additional Information

This experiment builds upon my extensive research in neuromorphic computing, decision support systems (DSS), and real-time applications. My previous work has explored energy-efficient Spiking Neural Networks (SNNs) and biologically inspired computing as scalable alternatives to traditional AI models.

The exponential growth of AI technologies, particularly Large Language Models (LLMs), has revealed critical energy inefficiencies. For example, modern LLMs consume megawatt-hours (MWh) of electricity to perform tasks that the human brain accomplishes with just 20 watts of power. Similarly, neuromorphic chips already demonstrate significantly lower energy consumption compared to traditional Convolutional Neural Networks (CNNs) running on NPU (Neural Processing Unit – Special-purpose hardware designed to accelerate AI and machine learning tasks by optimizing neural network computations) and GPU (Graphics Processing Unit – A versatile processor originally designed for rendering graphics, now widely used for parallel processing tasks and massive matrix multiplications for AI model training and inference) platforms for tasks like image recognition and pattern detection. These examples underline the potential of neuromorphic approaches to redefine AI performance metrics.

Our research will explore the implementation of neuromorphic systems, such as SNNs and other innovative architectures, in mission-critical scenarios where low latency and high adaptability are essential. (mission-critical situations – scenarios where decisions must be made rapidly and accurately, often under time constraints and with incomplete information. Centralized computing or oversight is impractical; systems must autonomously infer and act as if their "survival" depends on it.) In particular, the ability to process sparse, event-driven data efficiently could unlock new paradigms in real-time decision-making under constrained conditions.

The experiment will also validate the scalability and real-world applicability of traditional AI models versus neuromorphic approaches like SNNs and Oscillatory Neural Networks (ONNs). By doing so, we aim to establish a foundation for sustainable, decentralized, safe, and democratized AI systems that are resilient to the challenges of centralized computing, such as energy waste, security risks, and privacy concerns.

For a comprehensive overview of my publications and ongoing research contributions, please visit my ResearchGate profile.

My ResearchGate Profile.

This image represents an earlier prototype developed during my research on dynamic systems and real-time signal processing. Building physical models allows me to validate theoretical concepts and gain hands-on insights, an approach that will be integral to this project's development of energy-efficient neuromorphic AI systems.