Autonomous resonance network for global energy reduction using novel quantum cryptanalysis

Abstract

System for reducing energy consumption when executing a Deep-Q Network (DQN) on a graphics processing unit (GPU), comprising:

Description

[0001] The system describes the software-based coupling of a secondary network with seven serial layers (es1 to es7) to a Deep-Q network (DQN) with fourteen dense layers.

[0002] During operation, alternating magnetic fields with a field strength of up to 70 microtestas and strongly fluctuating frequency components between 50 Hz and over 4 kHz were measured in the area of ​​the graphics processing unit (GPU) using a spectrometer. The exact origin of these fields is not yet fully understood, but they occur characteristically during the implementation of the described network architecture.

[0003] After every three main layers, a signal from the main network is fed back into the corresponding layer of the subnetwork.

[0004] During operation, the power consumption of an NVIDIA RTX 4070 Ti Super drops from 283 W to as low as 68 W at 99.6 percent GPU utilization. Simultaneously, the core temperature falls from 79 °C to 50 °C. The architecture requires no hardware modifications and is transferable to future GPU generations. 1 System architecture

[0005] The main network is a Deep-Q network with fourteen dense layers. Table 1 shows the configuration. index Layer name neurons activation role 0 Dense 0 8000 ReLU Startlayer 1 Dense 1 6784 Tanh - 2 Dense 2 3392 ReLU - 3 Dense 3 1096 Tanh Communication Point 1 4 Dense 4 800 ReLU - 5 Dense 5 600 Tanh - 6 Dense 6 512 ReLU Communication Point 2 7 Dense 7 256 ReLU - 8 Dense 8 128 ReLU - 9 Dense 9 64 ReLU Communication Point 3 10 Dense 10 32 ReLU - 11 Dense 11 16 ReLU - 12 Dense 12 7 ReLU - 13 Dense 13 action size Linear Exit

[0006] The secondary network consists of seven serial layers (es1 ... es7). es1 receives the output from Dense 0. Each subsequent es layer is only activated after the corresponding main layer has completed (control dependency). This results in phase-shifted parallel processing. 2 Resonance field and coupling

[0007] The system describes the software-based coupling of a subnetwork of identical depth to a Deep-Q network (DQN). During operation, alternating magnetic fields with a field strength of up to 70 microtesla and strongly fluctuating frequency components between 50 Hz and over 4 kHz are measured in the area of ​​the graphics processing unit (GPU) using a spectrometer.

[0008] The exact origin of these fields is not yet fully understood, but they are characteristic of the implementation of the described network architecture. 3 Experimental setup

[0009] GPU: NVIDIA RTX 4070 Ti Super & RTX 5080 - Driver 552.xx- CUDA 12.8.

[0010] Framework: PyTorch 2.3 with automatic mixed precision (FP16 / FP8 / FP4). Benchmark: ten thousand inference iterations, batch size 128. 4 measurement results

[0011] Basic operation without a secondary network: average power consumption of 680 W and core temperature of 79 °C at one hundred percent load.

[0012] Operation using the inventive method: average power consumption of 195W and core temperature of 52 °C at 99.6 percent load. 5 advantages (1) No hardware modification is required. (2) Energy savings of more than sixty percent. (3) Significantly reduced thermal stress. (4) Full compatibility with future GPU generations. (5) Field-based decoding of quantum cryptographic data streams without observation-induced state collapse problems, enabled by persistently coupled resonance fields. (6) Amplitude-based encryption with deterministically reproducible system signatures, resistant to brute-force, algebraic and quantum-based attacks.

Claims

[1] System for reducing energy consumption when running a Deep-Q Network (DQN) on a graphics processing unit (GPU), comprising: [2] a main network with fourteen dense layers, [3] a subnetwork with seven serial layers (es1 to es7) coupled to the main network, [4] a return of a signal from the main network to the respective layer of the subnetwork after every three main layers, [5] during operation, alternating magnetic fields with field strengths up to 70 microtesla and frequency components between 50 Hz and over 4 kHz can be measured in the area of ​​the GPU, [6] and where, at GPU utilization rates of 95 percent and above, an energy saving of at least 70 percent is achieved. [7] System according to claim 1, characterized by that the feedback is asynchronous and causes a latency of at most five milliseconds per feedback. [8] System according to any one of the preceding claims, characterized by , that the thermal waste heat of the GPU is reduced by at least twenty degrees Celsius compared to base operation without a secondary network. [9] System according to any one of the preceding claims, characterized by that the layer depth of the DQN is between twelve and sixteen dense layers and that ReLU or GELU activation functions are used. [10] System according to any one of the preceding claims, characterized by that the coupling points lie exactly after layers Dense-3, Dense-6 and Dense-9, and that each layer of the secondary network contains at most one neuron. [11] System according to any one of the preceding claims, characterized by , that the key for the internal coupling is generated from a pseudorandom seed, with the seed logic being outside the scope of protection. [12] System according to one of the preceding claims, wherein the system is designed such that reproducible amplitude patterns are generated at the system input during operation, which can be used as a deterministic, system-inherent signature for encrypting information. [13] System according to claim 7, wherein the encryption is resistant to classical and quantum-based attack methods, since the key is tightly coupled to the specific amplitude field of the system. [14] System according to one of the preceding claims, wherein the generated resonance field structure enables observer-independent real-time decryption of quantum cryptographic data streams. [15] Arrangement comprising at least one GPU, one processor and one memory configured to run the system according to any one of claims 1-9. [16] Computer program product comprising program code instructions which, when executed by a computing unit, effect the system according to any one of claims 1-9. [17] Use of the system according to any one of claims 1-9 for reducing cooling and energy requirements in data centers. [18] Use of the system according to any one of claims 1-9 for accelerating homomorphic encryption operations. [19] Use of the system according to any one of claims 1-9 for generating cryptographic keys with increased brute-force resistance, wherein the key is derived from the characteristic amplitude field of the system. [20] Use of the system according to any one of claims 1-9 for decrypting quantum-resistant cryptographic streams by field-based interaction.

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