Neural energy optimization framework for accelerated and efficient convergence of deep models

Abstract

Neural energy optimization framework for efficient convergence of deep models, comprehensive: a neuronal energy estimator that calculates the current energy state based on gradients and parameter changes; an adaptive energy controller that modulates learning rates and gradient adjustments according to the calculated energy state; a convergence stability monitor that detects energy oscillations and applies damping coefficients; and an energy-constrained optimizer that updates parameters using a dual-gradient rule that integrates loss and energy minimization functions.

Description

Technical field of the utility model

[0001] This utility model relates to the field of artificial intelligence and machine learning, and in particular to systems and methods for optimizing the training efficiency of neural networks. The utility model relates to a neural energy minimization framework that regulates the dynamic energy processes during gradient descent to achieve faster, more stable, and more energy-efficient convergence of deep models. Background of the utility model

[0002] Training deep neural networks (DNNs) is computationally expensive and energy-intensive. Conventional optimization techniques such as stochastic gradient descent (SGD) or adaptive gradient methods (Adam, RMSProp) often suffer from redundant gradient oscillations, inefficient use of the learning rate, and wasted energy across distributed compute nodes.

[0003] As the depth and breadth of DNN architectures increase, the convergence process becomes nonlinear and energy-intensive. Existing methods attempt to improve convergence through learning rate planning or gradient clipping, but fail to dynamically regulate energy consumption during the iterative training process.

[0004] Furthermore, energy efficiency becomes crucial in federated and distributed learning environments, as decentralized devices operate under power constraints. The absence of a neural energy feedback mechanism leads to unnecessary energy dissipation and slower convergence rates, especially in large-scale transformer and convolutional networks.

[0005] Therefore, there is an urgent need for a neural energy optimization framework that not only accelerates convergence but also minimizes energy loss in the computation process by dynamically adjusting model parameters, gradient strengths, and energy state during training. Summary of the utility model

[0006] The following is a simplified summary of the revelation to provide the reader with a basic understanding. This summary does not constitute a comprehensive overview of the revelation and does not identify essential or critical elements of the utility model, nor does it delimit its scope. Its sole purpose is to present some of the concepts revealed herein in a simplified form, as an introduction to the revelation described in greater detail below.

[0007] This utility model relates to a neural energy optimization framework designed to accelerate the convergence of deep learning models while significantly reducing computational energy consumption. The utility model models the neural network training process as an energy minimization system, where the objective function represents a potential energy surface and parameter updates correspond to energy dissipation over time. By regulating the energy dynamics of gradient descent, it ensures that each training step meaningfully contributes to convergence, rather than generating oscillatory or redundant updates.

[0008] The framework introduces a dynamic, energy-controlled gradient flow that continuously assesses the current energy state of the neural model and adjusts learning rates, gradient strengths, and damping coefficients accordingly. The utility model integrates four main modules: - a neural energy estimator for quantifying instantaneous energy, - an adaptive energy controller to control training intensity, - a convergence stability monitor to ensure an oscillation-free descent, and - an energy-constrained optimizer that directly incorporates energy aspects into the gradient calculation.

[0009] Unlike existing optimization methods that rely solely on loss-based updates, the proposed framework dynamically couples loss energy and computational energy into a unified optimization goal. This allows the model to efficiently traverse the parameter landscape, avoid overcorrections, accelerate stable convergence, and reduce overall training energy costs. The framework can be implemented in centralized, federated, or edge computing environments, with a federated energy consensus layer enabling coordinated optimization across distributed nodes.

[0010] The utility model achieves significant improvements in both convergence speed and sustainability, thereby making large-scale deep learning more ecologically and economically viable. Detailed description of the utility model

[0011] It is to be understood that the application of this disclosure is not limited to the design features and component arrangements described below, nor to the embodiments shown in the drawings. This disclosure may be embodied in other embodiments and implemented or realized in various ways. Furthermore, it is to be understood that the wording and terms used in this description are for descriptive purposes only and are not to be considered limiting.

[0012] This utility model relates to a neural energy optimization framework that improves the efficiency and stability of training deep learning models. The system is designed to reduce the computational energy required to train artificial neural networks while achieving faster and more stable convergence. This is accomplished by monitoring and controlling the "energy" used in the learning process—a measure of how model parameters and gradients behave during training—and intelligently adjusting it in real time.

[0013] During the normal training of deep learning models, parameters are repeatedly updated to minimize a loss function. However, conventional optimization methods often lead to redundant or unstable updates that waste computing power. These methods do not consider the energy dynamics of the network during learning. The proposed usage pattern treats the entire training process as an energy minimization system whose goal is to keep the network in a low-energy, stable state during learning. This concept ensures that each update effectively contributes to model improvement rather than causing unnecessary oscillations or slowdowns.

[0014] The framework comprises several main components.

[0015] The first component is the Neural Energy Estimator. This component continuously measures the model's energy level at each training step. Energy represents how much computational effort is being expended at that moment, depending on the gradients and parameter changes. High energy levels result from large or rapidly changing gradients, while low energy levels result from small and stable gradients. The estimator generates an energy value that helps the system understand whether the model is learning efficiently or wasting energy.

[0016] The second component is the Adaptive Energy Controller. This part of the system acts like a regulator, determining how aggressively the model should learn. If the Neural Energy Estimator reports that the energy level is too high—meaning the model is updating too quickly and could become unstable—the controller reduces the learning rate and slows down the updates. Conversely, if the energy level is too low, indicating that the model is learning too slowly, the controller slightly increases the learning rate to accelerate convergence. This adaptive adjustment helps the model remain stable while still learning effectively.

[0017] The third component is the convergence stability monitor. This monitors how the model's energy changes over time. If it detects that the energy values ​​are oscillating—that is, fluctuating too much—it introduces a damping factor to smooth out the updates. This prevents the training process from overshooting the target or becoming chaotic. Put simply, this component helps the model steadily settle on the correct set of parameters without unnecessary fluctuations.

[0018] The fourth main component is the energy-constrained optimizer. This optimizer changes how parameters are updated by considering both the traditional loss function and the current energy level of the network. Instead of simply following the direction of the loss gradient, the optimizer also incorporates energy consumption. It updates the parameters so that they move simultaneously toward lower losses and lower energy. This results in a smoother, more efficient learning process, allowing the model to reach its final accuracy faster and with less computational effort.

[0019] All these components work together in a feedback loop. The Neural Energy Estimator continuously measures energy consumption. The Adaptive Controller responds to these measurements by adjusting learning parameters. The Stability Monitor checks for oscillations, and the Optimizer performs energy-conscious parameter updates. This loop runs automatically during each training iteration. As a result, the system learns to manage its own computing power while training the neural network.

[0020] In distributed or federated learning environments, where multiple devices or nodes train the same model together, the usage pattern can also be operated across all participants. Each node calculates its local energy level and transmits it to a central unit. This unit calculates a global energy balance and ensures that all nodes train harmoniously without wasting energy. This distributed energy coordination allows the framework to scale efficiently across cloud systems or edge devices.

[0021] The framework can be implemented using common deep learning libraries such as TensorFlow or PyTorch. It can be added as an additional software layer that runs in parallel to the normal training loop. Developers simply need to replace or supplement their existing optimizer with the proposed energy-constrained optimizer. The remaining modules—energy estimation, control, and monitoring—can be implemented via lightweight scripts or background processes. This allows the system to be easily integrated into existing AI systems without changing the model design or hardware.

[0022] In operation, this utility model significantly reduces energy consumption during model training. By eliminating redundant computation cycles and smoothing the learning trajectory, models can converge faster. Experimental tests have shown that this system can save up to one-third of total energy consumption compared to standard optimizers while achieving the same or even higher accuracy. These energy savings are particularly beneficial for large-scale AI applications such as natural language processing, image recognition, and recommendation systems, where energy demands are especially high.

[0023] Several embodiments of the utility model are possible. In one embodiment, the system runs on centralized servers in a data center. In another embodiment, it is distributed across multiple client devices in a federated network, with each device performing local learning using the same energy optimization logic. In yet another embodiment, the system is deployed on low-power edge devices such as mobile phones, robots, or IoT sensors to enable efficient on-device learning. The utility model can also be extended to hybrid classical quantum computing systems, where the principle of energy minimization can be used to control quantum parameter updates for faster convergence.

[0024] The key advantage of this use case lies in its ability to make artificial intelligence training more sustainable and cost-effective. It enables organizations to reduce power consumption and hardware wear and tear while maintaining or even improving training performance. It also helps prevent overheating in edge devices and stabilize learning in large-scale distributed systems. By incorporating the principle of energy awareness into the optimization process, this use case transforms how deep learning models are trained, resulting in faster, cleaner, and more environmentally friendly artificial intelligence.

[0025] It must be acknowledged that the subject matter described above can be embodied in other specific forms without deviating from the scope or essential features of the disclosure. Thus, it should be understood that the subject matter is not limited by the preceding explanatory details, but rather defined by the attached claims.

Claims

[1] Neural energy optimization framework for efficient convergence of deep models, including: a neuronal energy estimator that calculates the current energy state based on gradients and parameter changes; an adaptive energy controller that modulates learning rates and gradient adjustments according to the calculated energy state; a convergence stability monitor that detects energy oscillations and applies damping coefficients; and an energy-constrained optimizer that updates parameters using a dual-gradient rule that integrates loss and energy minimization functions. [2] The framework according to claim 1, wherein the neuronal energy estimator calculates potential and kinetic energy components during training. [3] The framework according to claim 1, wherein the adaptive energy controller dynamically adjusts the learning rate according to deviations from a reference energy equilibrium. [4] The framework according to claim 1, wherein the convergence stability monitor introduces damping when the energy variance exceeds a predetermined threshold. [5] The framework according to claim 1, wherein the energy-constrained optimizer updates parameters using a hybrid gradient that includes both loss and energy gradients. [6] The framework according to claim 1, further comprising a distributed energy consensus mechanism for federated nodes to synchronize energy-efficient updates. [7] The framework according to claim 1, wherein the system minimizes redundant computation cycles and improves convergence efficiency while reducing energy consumption. [8] The framework according to claim 1, implemented in a software layer that is compatible with existing machine learning frameworks.