ENERGY EFFICIENCY IN NEURON NETWORKS: PROBLEMS OF OPTIMIZING LARGE MODELS

SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 11 NOVEMBER 2025 ISSN: 2181-3337 | SCIENTISTS.UZ 59 ENERGY EFFICIENCY IN NEURON NETWORKS: PROBLEMS OF OPTIMIZING LARGE MODELS M. Tursunaliyeva 3rd year student, Fergana State University https://doi.org/10.5281/zenodo.17674536 Abstract. This article analyzes technical and economic problems associated with increased energy consumption by large neural networks. The fact that modern AI models have trillions of parameters requires enormous computing power in their training and inference processes, which leads to increased energy consumption and increased infrastructure costs. The article examines the technical essence of quantization, practical results, and its role in optimizing large models. Keywords: neural networks, energy efficiency, model compression, quantization, FP32, INT8, large language models, optimization, artificial intelligence, computational costs. Introduction Today, artificial intelligence has penetrated almost every aspect of our lives - from assistants on our phones to large models used in research. But there is one important point: the smarter these technologies are, the more energy they consume. In particular, systems with trillions of parameters, such as GPT, LLaMA, or other large language models, require enormous computing power. This, of course, leads to an increase in energy consumption, an increase in the heat output of servers, an increase in the ecological footprint, and, unfortunately, the need for very expensive infrastructure. For this very reason, the energy efficiency of neural networks has become one of the most pressing problems today. In this article, we will consider the main causes of this problem and analyze one of the effective solutions used in practice - model compression and, in particular, quantization technique. Why do large neural networks require so much energy? The main reason lies in their internal structure. Each model has millions or even billions of parameters, and each mathematical operation between them is performed on high-energy devices, such as GPUs/TPUs. The larger the model, the more matrix products, normalization processes, activation functions, and other calculations are performed, which increases the number of powerful graphics processors constantly operating on server farms. The training process is especially the largest energy consumer, as the model repeats heavy operations like backpropagation over millions of iterations each time. However, inference - that is, using the model - also requires energy, since each query requires passing through all layers of the model. As a result, large neural networks require higher levels of cooling, ultra-resistant servers, and more power supply. This problem is becoming not only a technical, but also an economic and environmental issue. Therefore, the development of energy-efficient approaches is crucial for the future of AI technologies. The main problem of large neural networks is a sharp increase in the demand for their computing resources. Since the models contain billions of parameters, each training stage performs huge matrix multiplication, which leads to GPU clusters operating at a constant high voltage. As a result of this process, server centers consume a large amount of energy, and the main part of this SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 11 NOVEMBER 2025 ISSN: 2181-3337 | SCIENTISTS.UZ 60 energy goes to cooling systems, since working graphics processors release a significant amount of heat. The problem is not only technical - there is also an economic side. Modern training clusters are very expensive: they require hundreds or thousands of GPUs, which require not only large investments, but also high costs for continuous operation. Also, the carbon footprint, which arises as a result of training large models on a global scale, is seriously criticized in scientific circles. Thus, with an increase in model parameters, energy consumption, environmental impact, and infrastructure costs increase exponentially. This necessitates the search for new technologies aimed at making neural networks more efficient. One of the most effective approaches to the energy-efficient use of large models is model compression techniques. Among them, one of the most commonly used and practically effective methods is quantization. The main idea of quantization is that the calculation volume can be significantly reduced by expressing weights and activations in the model in smaller bits, such as 8-bit or 4-bit, from the 32-bit floating-point format. Simply put, quantization "creates a lighter version of the model while maintaining its accuracy." If the model weights are switched from 32-bit to 8-bit, this will not only reduce memory requirements by 4 times, but also significantly simplify operations performed on GPUs/TPUs. As a result, the inference process is accelerated and energy consumption is reduced. In some cases, it has also been observed that energy consumption can be reduced by 6-7 times through 4-bit quantization. The advantage of quantization is that it does not change the overall architecture of the model. That is, the model remains the same in form, but becomes "lighter." The following simple diagram illustrates the gist of quantization: 32-bit model parameters [0.245893] [1.983422] [0.000184] [3.294524] │ ▼ 8-bit quantized version [0.24] [1.98] [0.00] [3.29] Although these changes may seem small, they provide enormous energy savings for models with millions of parameters. In practice, a quantized model puts less pressure on the server, reduces heat output, reduces energy consumption of cooling systems, and, of course, significantly reduces infrastructure costs. To better imagine the practical result of quantization, let's look at a real example. Imagine that you have a 1 billion-parameter neural network. This model requires approximately 4 gigabytes of memory when stored in the classic FP32 (32-bit float) format. But if we compress it through 8-bit quantization, the memory requirement drops to 1 gigabytes. This means that the model will be 4 times lighter than before. Energy consumption also decreases accordingly, since weights expressed in small bits are read faster by the GPU, fewer transistors operate, and this directly leads to a decrease in energy consumption. The following simple diagram illustrates how the quantization process works, both simply and efficiently: As you can see in the diagram, the model itself does not change its appearance - the number of layers, architecture, functions, or outputs are the same. Only the method of parameter representation will change. As a result, energy efficiency increases, servers work lighter, the model's response speed increases, and costs are significantly reduced in practice. For this reason, quantization has become one of the most widely used optimization methods in industry today. SCIENCE AND INNOVATION INTERNATIONAL SCIENTIFIC JOURNAL VOLUME 4 ISSUE 11 NOVEMBER 2025 ISSN: 2181-3337 | SCIENTISTS.UZ 61 The increasing size of artificial intelligence models is creating many new opportunities for us, but it also creates problems such as energy consumption, price, and environmental impact. The good thing is that effective techniques for solving these problems already exist. One of them - quantization - makes models "lighter," making them more economical, faster, and economically more convenient. Conclusion Currently, the sustainable development of AI technologies relies on such solutions. If we want to create more intelligent, powerful, but at the same time environmentally and economically responsible systems in the future, it is very important to pay attention to energy efficiency. A welloptimized model not only saves resources but also contributes to the further popularization of convenient, fast, and modern technologies for everyone. REFERENCES 1. Goodfellow, I., Bengio, Y., Courville, A. Deep Learning. Cambridge: MIT Press. 775 p. 2. Jacob, B. et al. Quantization and Training of Neural Networks for Efficient IntegerArithmetic-Only Inference // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . - 2018. - P. 2704-2713. 3. Han, S., Mao, H., Dally, W. J. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding // International Conference on Learning Representations (ICLR) . - 2016. 4. Jouppi, N. P. et al. In-Datacenter Performance Analysis of a Tensor Processing Unit // Proceedings of the 44th Annual International Symposium on Computer Architecture (ISCA) . - 2017. - P. 1-12. 5. Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks // European Conference on Computer Vision (ECCV) . - 2016. - P. 525-542. 6. OpenAI. GPT-4 Technical Report. - 2021. - URL: https://openai.com (accessed: 20.11.2025). 7. Wu, J., Leng, C., Wang, Y., Hu, Q., Cheng, J. Quantized Convolutional Neural Networks for Mobile Devices // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . - 2016. - P. 4820-4828.