A COMPUTATION SYSTEM AND METHOD FOR SYNTHESIS OF WEIGHTS VIA TRACT ERROR DYNAMICS WITH ZERO MEMORY FOOTPRINT AND ADAPTIVE IMMUNITY PROTECTION IN ARTIFICIAL NEURAL NETWORKS.

Abstract

This invention is a method and system for synthesizing artificial neural network weights that eliminates the Von Neumann memory bottleneck, excessive energy consumption, and representation collapse caused by zero-loss forcing, which arise from the static storage of trillions of parameters in physical memory (DRAM) in deep artificial neural networks. Instead of storing synaptic weights as permanent memory matrices, the system synthesizes them in real-time as transient resonances derived from a second-order polynomial trajectory (zn+1 = zn^2 + c) in the complex number plane.The system includes an orbital error dynamics controller that prevents over-coherence collapse by performing homeostatic boundary surfing at the cardioid limit, a heavy-tailed stochastic jump operator that overcomes local gradient traps, an adaptive CD4+ regulatory immunity filtering mask that protects central coordinates by filtering out noise and intrusions in sensory streams, and a zero-storage (O(1) = 24 byte) weight projection architecture that maps the four quadrants of the complex plane to genetic bases.

Description

TARIFF ZERO MEMORY FOOTPRINT AND ORBIT ERROR IN ARTIFICIAL NEURAL NETWORKS WEIGHT SYNTHESIS AND ADAPTIVE THROUGH ITS DYNAMICS IMMUNE PROTECTION CALCULATION SYSTEM AND METHOD THE TECHNICAL FIELD TO WHICH THE INVENTION RELATES This breakthrough utilizes deep artificial neural networks, machine learning algorithms, and neural pathways. The invention relates to computing processor architectures and analog-optical computing fields. Specifically, the synaptic weight matrices in artificial neural networks relate to physical memory. eliminating the need to permanently store data in units (DRAM / SRAM), weights from dynamic manifold trajectories in a complex plane in real time synthesizing them as transient resonances, in response to hostile sensory attacks and noise. a computational method that uses an adaptive immune filtering mask and this It relates to the hardware system that executes the method. STATE OF THE ART (PRIVATE ART) Today's artificial intelligence and deep learning models (for example, Transformer and Big Brother) Language Models); billions or trillions of scalar weight parameters in silicon memory as static floating-point tensors in chips (DRAM / HBM) It is based on the principle of concealment. This classical architecture has three fundamental technical bottlenecks. It creates: Firstly, there are billions of connections between processor cores (GPU / TPU) and memory units. The Von Neumann memory wall arises from the continuous transfer of parameters. (Memory Wall) is a bottleneck. It's the electricity consumed in modern artificial intelligence data centers. More than 85% of its energy comes not from arithmetic operations, but from static weight tensors. This is due to the physical transfer of information between the memory and the processor. Secondly, classical optimization methods (gradient descent) reduce the total error to zero. The download target is (L -> 0). Dynamic systems and non-equilibrium thermodynamics theory. According to thermodynamics, eliminating all internal deviations and stresses in a system is a process of thermodynamics. This corresponds to equilibrium and entropy death. In practice, the losses of artificial neural networks... Over-smoothing occurs when forced to zero, and when fed with synthetic data. irreversible model collapse and at the slightest hostile noise (adversarial perturbation) Problems arise where the decision surfaces become scattered. 1 Thirdly, there is the structural contradiction between biological morphogenesis and artificial systems. Although the human genome contains approximately 750 megabytes of digital information, it is worth 100 billion dollars. It governs the development of neurons and 100 trillion synapses. Biological life has no static aspects. It does not store the weight tensor; it is based on procedural and recursive seeds. It generates functions when needed. THE PURPOSE OF THE INVENTION AND THE TECHNICAL PROBLEMS IT SOLVED. The main purpose of the invention is to permanently define weight matrices in artificial neural networks. independent of layer depth, eliminating the need for storage in physical memory. weight synthesis that always works with a constant memory footprint O(1) = 24 bytes. The goal is to improve the system. Another objective of the invention is to lock the system to a zero-failure point (static equilibrium). instead, the model performs homeostatic boundary surfing on the boundary of the Mandelbrot parameter space. The trajectory that ensures continuous dynamism between chaotic divergence and the collapse of representation. The goal is to provide a fault dynamics controller. Another aim of the invention is to investigate local gradients in non-convex missing spaces. zinc in mammalian fertilization to escape traps and saddle points A stochastic jump with a heavy-tailed Cauchy distribution, inspired by the spark phenomenon. The goal is to provide the operator. Another aim of the invention is to detect sudden dispersion outside of sensory streams (OOD). by suppressing noises and hostile attack toxins, the central coordinate seed A pair containing a protective adaptive CD4+ regulatory T-cell immune filtering mask. The goal is to provide a brain (cranial-enteric) cybernetic architecture. One ultimate goal of the invention is to use the method not only with digital microprocessors, but also with spatial capabilities. in analog optical processors using light modulators (SLM) and optical Fourier lenses The goal is to make it possible to perform this operation at the speed of light (sub-nanoseconds) with zero thermal loss. BRIEF DESCRIPTION OF THE FIGURES Figure 1: Boundary geometry of the parameter space subject to the invention, cardioid cusp singularity, Schematic view showing shoulder turning loci and radial escape flows. Figure 2: Static equilibrium death, conservative harmonic oscillation, and open break at singularity. Morphophysical comparison graphs of thermodynamically bent sine waves. Figure 3: Heavy-tailed surging a non-convex potential energy barrier. Principle diagram of a biomimetic stochastic jump operator (tunneling). 2 Figure 4: Internal hyperbolic collapse, chaotic divergence burst, and dynamic phase space. Active OED limit surfing limit cycle portrait. Figure 5: Central / Cranial processor, Enteric sensory processor, and adaptive intermediary system. Dual Brain cybernetic block diagram including CD4+ immune filtering mask. Figure 6: The 4 quadrants of the complex plane: Adenine, Thymine, Cytosine, and Guanine Weight vector derivation geometry without matrix storage, by mapping to biological bases. Figure 7: Loss convergence of 5 independent seeded empirical validation experiments. Noise attack resilience and stable O(1) memory footprint graphs. LIST OF REFERENCES IN THE FIGURES (1) Central Coordinate Generator / Cranial Processing Unit (2) Orbital Error Dynamics and Homeostatic Boundary Controller (3) Heavy Tail Biomimetic Stochastic Jump Operator (4) Enteric / Visceral Sensory Flow Processor (5) Adaptive CD4+ Regulatory Immune Filtering Mask (6) 4-Quadrant Genetic Weighting Discriminant Unit (7) Temporary Operational Weight Buffer (O(1) = 24 Bytes) (8) Analog Optical Spatial Light Modulator (SLM) (9) 4f Optical Fourier Lens (10) Dark Basin CMOS Photodetector Array DETAILED DESCRIPTION OF THE INVENTION The invention describes a method and system for analyzing the synaptic weights of an artificial neural network. Instead of storing it in physical DRAM matrices, it uses a compact three-element parameter. The coordinate seed Θ = (c_x, c_y, ζ) ∈ ℝ³ is generated procedurally, where c = c_x + i c_y ∈ ℂ represents the complex coordinate, and ζ represents the zoom scale. The total persistent storage requirement is only 3 x 64-bit floating-point numbers. It consists of exactly 24 bytes (O(1)). 3 The processor unit (1) iterates through the complex quadratic polynomial in the given coordinate Θ. The function z_{n+1} = z_n^2 + c (z_0 = 0) is executed. The sampling area is divided into four geometric quadrants: Quadrant 1 (+Re, +Im), Quadrant 2 (-Re, +Im), Quadrant 3 (-Re, -Im) and Quadrant 4 (+Re, - Im). The separating unit (6), the mass of the dark areas that do not escape, falling on the quadrants. It calculates the ratios R_1, R_2, R_3, R_4 ∈ [0, 1]. These ratios are related to biological genetic bases. by pairing; w_1 = 2R_1 - 1 (Adenine / Stimulant Weight), w_2 = 2R_2 - 1 (Thymine / Inhibitory Weight), w_3 = 2R_3 - 1 (Cytosine / Cross Interaction) and b = 2R_4 - 1 (Guanine / Deviation Value) is transferred to the temporary weight buffer (7). After performing forward propagation calculations on the input vector, the total loss is calculated. The function is calculated as: L_{total} = L_{task} + λ_{orb} L_{orbital}. Here L_{orbital} The term = ((N_{esc} - N) / N)^2 represents the parameters of the analytical cusp of the Mandelbrot cardioid. remaining within the boundary corridor around point (c = 1 / 4) and shoulder loci (0.25 ± 0.18i) It monitors. Once forward and backward propagation is complete, temporary weights are cleared from memory; the system It always reverts to a 24-byte memory state. During optimization, the norm of the gradient vector is set to a predetermined threshold value. When it falls below (||∇_Θ L|| < ε_tol) and the task error persists, gradient stagnation occurs. is detected. At this moment the jump operator (3) activates and the parameter seed is heavily loaded. It inserts a tunneling vector derived from the tailed Cauchy distribution (ξ ~ Cauchy(0, γ)). (Θ_{t+1} = Θ_t + Ω_{tunneling}). This process involves local non-convex potential It allows them to overcome barriers and leap into global watersheds (Figure 3). In the Dual Brain architecture of the system (Figure 5); the central processor (1) is low frequency When generating global parameter decisions, the enteric sensory processor (4) provides real-time high-performance solutions. It processes high-frequency environmental data streams. Hostile noises or sensor shocks Adaptive CD4+ immune filtering to prevent disruption of central coordinates. The mask (5) is activated: M_{CD4} = 1 / [1 + exp(α(|∇_W L_{visc}| - τ))]. The filtered gradient (ΔW_{shielded} = ∇L_{visc} ⊙ M_{CD4}) is only tolerable. This allows adaptive corrections, to a manageable level, to be reflected in the central model. THE FORM OF APPLYING THE INVENTION TO INDUSTRY The invention relates to systems and methods used in smartphones and IoT edge computing (Edge AI). devices with memory and power limitations, such as microcontrollers and autonomous vehicle in-vehicle chips It can be directly applied to all kinds of digital equipment. Also, spatial light modulator (8), Ultra-fast operating at the speed of light by integrating a Fourier lens (9) and a photodetector array (10) It can also be integrated into low-energy analog optical artificial intelligence processors. 4

Claims

REQUESTS 1. In artificial neural networks, synaptic weight matrices are stored in permanent memory without... It is a computational method that synthesizes information procedurally; its characteristic feature is: • A three-dimensional parameter coordinate is generated by a central coordinate generator (1). Determination of the seed (Θ = (c_x, c_y, ζ)), • Complex quadratic polynomial around the specified coordinate seed. The complex plane mass distribution is obtained by running the iteration (z_{n+1} = z_n^2 + c). sampling, • The sampled population distribution is analyzed through a 4-quadrant genetic segregator (6) into four by dividing into quadrants and the non-escape mass ratios of each quadrant (R_1, R_2, R_3, R_4) operational weight and deviation vector (W = [w_1, w_2, w_3, b]^T) temporary Synthesis in buffer (7), • Artificial neural network forward propagation via the synthesized temporary weight vector. execution of the process, • The temporary weight in question after forward propagation and gradient update. By deleting the vector from memory, the persistent storage footprint remains constant at the O(1) level. It includes the steps involved in the process of being held.

2. It is a method according to claim 1, and its characteristic is that the coordinate seed (Θ) in question During optimization, the parameters are located at the Mandelbrot cardioid limit. The singularity (c = 1 / 4) and the fact that it remains around the shoulder turning loci, and Lyapunov An orbital error controller that performs homeostatic boundary surfing by keeping its base close to zero. (2) is included.

3. It is a method according to claim 1, and its characteristic is that it uses the gradient norm in the optimization process. if it falls below a specified pause threshold value (||∇_Θ L|| < ε_tol), parameter seed from heavy-tailed Cauchy distribution (Ω_{tunneling} ~ Cauchy(0, a biomimetic stochastic jump to which a jump vector derived from γ)) is added It is characterized by the triggering step of the operator (3).

4. It is a method according to claim 1, and its feature is that it receives real-time sensory data stream (4) evaluating incoming gradients and identifying hostile noise or off-dispersion interference. in these cases, by damping the gradient intensity through a sigmoid threshold function An adaptive CD4+ regulatory immune filter that preserves the central coordinate seed. The mask includes (5) steps.

5. It is a method according to claim 1, and its feature is the 4-quadrant genetic segregator (6) In step 1, the stimulus weight is mapped to the adenine base (w_1 = 2R_1 - 1), 2. The inhibitor weight (w_2 = 2R_2 - 1) is determined by mapping the thymine base to the 3rd quadrant, and the cytosine base to the 4th quadrant. Cross-interaction weight (w_3 = 2R_3 - 1) and Guanine of Quadrant 4 are mapped to the base. It is characterized by the step of deriving the deviation value (b = 2R_4 - 1) by mapping it to the base. It is done.

6. It is a method according to claim 1, the characteristic of which is; weight synthesis and advancement. propagation processes; a spatial process that phase-encodes the coordinate seed to the laser wavelength. light modulator (8), a 4f optical lens (9) implementing continuous 2D Fourier transform and a dark basin photodetector array that converts optical density to synaptic current (10) It is performed via an analog optical medium.

7. An artificial neural network configured to execute method steps 1 through 6. It is a network computing system; its characteristic feature is: • A central processing unit that stores a three-element parameter coordinate seed (Θ). (1), • A trajectory error controller that ensures parameters remain within the cardioid limit. (2), • A heavy-tailed stochastic jump that kicks in at gradient pauses. operator (3), • An enteric processor that processes high-frequency sensory flows (4), • Adaptive CD4+ immune filtering that dampens sensory gradient shocks. circuit (5), • A 4-quadrant decomposer (6) that converts complex plane mass ratios into weights. And • A temporary structure that holds the weights during the forward spreading phase and is emptied when the process is complete. It contains a weight buffer (7).

8. When executed by a computer or microprocessor, the method in Prompts 1 to 5 It is a computer program that contains commands enabling the execution of certain steps. 6

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