Derivative works

Defensive Disclosure Document

Publication Date: May 9, 2026
Reference Patent: US 11,328,206 B2
Title: Derivative Implementations of Deep Neural Network-Based Control Systems for Computing Devices
Keywords: Deep Neural Network, Processor Control, Anomaly Detection, Generative Models, Real-Time Optimization, Predictive Control, Low-Power Computing, System-on-Chip, Edge AI, Federated Learning.

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Abstract

This document discloses a series of derivative implementations and alternative embodiments of the inventions claimed in US Patent 11,328,206. The purpose is to establish prior art for subsequent incremental improvements in the field of using deep neural networks (DNNs) for real-time management and optimization of computing hardware. The disclosures herein detail variations in componentry, operational envelopes, cross-domain applications, integration with emerging technologies, and failure-mode operations, thereby rendering them obvious to a person having ordinary skill in the art.

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Disclosure 1: Derivatives of Independent Claim 20 (Processor with DNN Control Unit)

The core concept is a processor with a control unit that includes a DNN to manage the datapath based on learned workload behavior.

1.1 Material & Component Substitution

• Derivative 1.1.1: Neuromorphic Co-Processor Control Unit

  • Enabling Description: The conventional CMOS-based DNN accelerator circuitry within the control unit is substituted with a neuromorphic co-processor fabricated from phase-change materials (e.g., Ge2Sb2Te5 - GST). This co-processor, based on a spiking neural network (SNN) architecture, processes sensor and instruction data as temporally sparse spike trains. The SNN's inherent event-driven and low-power nature allows the control unit to perform predictive power-gating of datapath functional units with significantly lower energy overhead than a traditional DNN. The SNN learns workload patterns by adjusting synaptic weights based on spike-timing-dependent plasticity (STDP) rules, offering a mechanism for online, unsupervised learning of processor behavior.

  graph TD
  A[Instruction Stream & Sensor Data] --> B{Spike Encoder};
  B --> C[GST-based SNN Co-Processor];
  C --> D{Spike Decoder};
  D --> E[Datapath Control Signals];
  C -- STDP Learning --> C;
  subgraph Control Unit
  B; C; D;
  end
  subgraph Processor Core
  E --> F[Processing Datapath];
  end

• Derivative 1.1.2: Optical DNN Inference Engine

  • Enabling Description: The electronic DNN in the control unit is replaced by an integrated silicon photonics inference engine. Inputs (e.g., performance counter values) modulate a set of micro-ring resonators, encoding data into light intensity. The light passes through a mesh of tunable interferometers that represent the learned weights of the DNN. Photodetectors at the output convert the resulting optical signals back into electronic control signals for the processor datapath (e.g., branch prediction overrides, cache prefetch commands). This substitution allows for inference at near the speed of light, drastically reducing the latency between observing a processor state and issuing a corresponding control command.

  sequenceDiagram
  participant EIC as Electrical-to-Optical Interface
  participant PIC as Photonic Integrated Circuit (DNN)
  participant OEC as Optical-to-Electrical Interface
  participant DP as Datapath Control
  EIC->>PIC: Modulates light with sensor data
  PIC->>PIC: Optical matrix multiplication via interferometer mesh
  PIC->>OEC: Light output representing inference result
  OEC->>DP: Generates electronic control signals
  ```

1.2 Operational Parameter Expansion

• Derivative 1.2.1: Cryogenic Superconducting DNN Controller for Quantum Computing

  • Enabling Description: The technology is adapted for controlling the classical-quantum interface in a quantum computer operating at cryogenic temperatures (e.g., < 100 mK). The processor control unit and its integrated DNN are implemented using superconducting logic circuits (e.g., Single Flux Quantum logic). The DNN receives inputs on qubit state measurement fidelities, decoherence times, and control pulse errors. It then generates corrective control signals to adjust microwave control pulses sent to the qubits, optimizing gate fidelities in real-time. The extreme low-temperature environment minimizes thermal noise, enabling the DNN to learn and react to subtle statistical drifts in quantum system behavior.

  stateDiagram-v2
  [] --> Monitoring: System Initialization
  Monitoring --> Analyzing: Qubit state data received
  Analyzing --> Predicting: DNN predicts gate fidelity drift
  Predicting --> Correcting: DNN issues new pulse parameters
  Correcting --> Monitoring: Control pulses adjusted
  state Analyzing {
  direction LR
  [] --> DNN_Inference
  DNN_Inference --> [*]
  }

• Derivative 1.2.2: High-Frequency DNN for RF Power Amplifier Control

  • Enabling Description: The DNN control system is scaled to operate at radio frequencies (GHz range) to manage a Gallium Nitride (GaN) power amplifier in a 5G/6G base station. The DNN is implemented on a high-speed FPGA. It receives inputs on the amplifier's temperature, input power, and load impedance mismatch (Voltage Standing Wave Ratio - VSWR). The DNN's output dynamically adjusts the amplifier's gate bias and supply voltage to prevent thermal runaway and maintain linearity under rapidly changing load conditions, maximizing power-added efficiency (PAE) and preventing component damage.

  graph TD
  subgraph RF_Frontend
  A[RF Input Signal] --> B[GaN Power Amplifier];
  B --> C[Antenna];
  end
  subgraph DNN_Control_Unit_FPGA
  D[Sensors: Temp, VSWR, Power] --> E{DNN Inference};
  E --> F[Gate Bias & Voltage Control];
  end
  F --> B;
  ```

1.3 Cross-Domain Application

• Derivative 1.3.1: Aerospace - Flight Control Actuator Management

  • Enabling Description: A federated set of DNN control units is embedded within the smart actuators of an aircraft's flight control surfaces (e.g., ailerons, rudder). Each DNN receives local sensor data (strain, temperature, vibration) and data from the central flight control computer (desired surface position). The DNN predicts incipient mechanical failure or performance degradation (e.g., hydraulic fluid cavitation) and generates control signals to operate the actuator in a way that mitigates stress and prolongs operational life, while still meeting flight control demands. It can also issue a warning signal for predictive maintenance.

  classDiagram
  class FlightControlComputer {
  +calculateSurfacePositions()
  }
  class SmartActuator {
  <>
  -localSensors
  -dnnModel
  +receiveCommand()
  +predictFailure()
  +adjustOperation()
  +issueMaintenanceWarning()
  }
  FlightControlComputer "1" -- "N" SmartActuator : sends commands

• Derivative 1.3.2: AgTech - Autonomous Irrigation System Optimization

  • Enabling Description: The processor is an embedded controller in a smart irrigation valve. The DNN receives inputs from soil moisture sensors, local weather station data (temperature, humidity), and satellite imagery indicating crop stress (e.g., NDVI). Based on a generative model of soil water absorption and crop evapotranspiration, the DNN outputs control signals to precisely modulate the valve's opening duration and frequency, optimizing water delivery to the plant root zone while minimizing runoff and evaporation. This enhances water use efficiency and crop yield.

  flowchart LR
  A[Soil Moisture Sensor] --> C{DNN Controller};
  B[Weather Data] --> C;
  D[Satellite NDVI Data] --> C;
  C --Control Signal--> E[Irrigation Valve];
  E --Water Flow--> F[Crop Field];

• Derivative 1.3.3: Consumer Electronics - Smart Appliance Energy Management

  • Enabling Description: A DNN-based control unit is integrated into a home appliance, such as a refrigerator or HVAC system. The DNN receives sensor data on internal temperature, door open/close events, and user interaction patterns. It also receives external data from the smart grid, including real-time electricity pricing and grid load. The DNN predicts periods of high energy cost or low user demand and adjusts the appliance's operational cycles (e.g., pre-cooling the refrigerator before a price spike) to minimize energy consumption and cost without compromising core functionality.

  sequenceDiagram
  participant User
  participant SmartGrid
  participant Appliance_DNN
  participant Compressor
  loop Real-time
  SmartGrid->>Appliance_DNN: Sends electricity price data
  User->>Appliance_DNN: Opens door (sensor input)
  Appliance_DNN->>Appliance_DNN: Predicts future cooling demand and cost
  Appliance_DNN->>Compressor: Issues optimal ON/OFF control signal
  end
  ```

1.4 Integration with Emerging Tech

• Derivative 1.4.1: AI-Driven Self-Tuning DNN Parameters

  • Enabling Description: The DNN control unit is paired with a secondary, higher-level AI model (e.g., a reinforcement learning agent). This agent observes the performance outcomes (e.g., power consumption, execution latency) resulting from the DNN's control signals. If the outcomes deviate from a desired optimal state, the reinforcement learning agent updates the weights and biases of the primary DNN control unit. This creates a closed-loop system where the processor's control logic continuously fine-tunes itself in response to new or evolving workloads, a process of online meta-learning.

  graph TD
  subgraph Processor
  A[Workload] --> B[Datapath];
  C[DNN Control Unit] --Control Signals--> B;
  A --Instruction Data--> C;
  B --Performance Data--> D[RL Agent];
  end
  D --Updated DNN Weights--> C;

• Derivative 1.4.2: IoT Sensor Fusion for Holistic System Control

  • Enabling Description: The processor's DNN control unit ingests data not only from its internal sensors but also from a network of external IoT sensors via a low-latency protocol like MQTT. For example, in an autonomous vehicle, the central processing unit's DNN controller would receive inputs from its own temperature sensors, as well as ambient temperature data from external sensors, LiDAR-detected road surface conditions, and IMU data. It would then generate control signals to adjust processor clock frequency to manage thermal output proactively, ensuring reliable performance during computationally intensive perception tasks.

  flowchart TD
  A[Internal Temp Sensor] --> F{Central DNN Controller};
  B[External IoT Temp Sensor] --> F;
  C[LiDAR Sensor] --> F;
  D[IMU Sensor] --> F;
  F --Adjust Clock Frequency--> G[Processor Datapath];

• Derivative 1.4.3: Blockchain-Verified DNN Model Updates

  • Enabling Description: To ensure security and integrity in a federated or safety-critical system, updates to the DNN model parameters are managed via a private blockchain. When a new, validated model is ready for deployment, its parameter hash is recorded on the blockchain. The processor's control unit will only accept and load a new set of parameters if their hash matches a transaction on the blockchain, which is cryptographically signed by an authorized entity (e.g., the device manufacturer). This prevents malicious actors from pushing compromised models to the device that could degrade performance or create security vulnerabilities.

  sequenceDiagram
  participant Manufacturer
  participant Blockchain
  participant Device_Controller
  Manufacturer->>Blockchain: Commits new DNN model hash (Transaction)
  Device_Controller->>Blockchain: Queries for latest model hash
  Blockchain-->>Device_Controller: Returns latest valid hash
  Device_Controller->>Device_Controller: Verifies received model update against hash
  alt Hash Matches
  Device_Controller->>Device_Controller: Applies DNN update
  else Hash Mismatches
  Device_Controller->>Device_Controller: Rejects update and logs error
  end
  ```

1.5 The "Inverse" or Failure Mode

• Derivative 1.5.1: Graceful Degradation Mode

  • Enabling Description: The DNN is specifically trained to recognize precursors to a non-recoverable hardware fault (e.g., voltage droop, critical temperature threshold). Upon predicting such an event, the DNN does not attempt to optimize performance but instead outputs control signals that place the processor into a safe, low-power state. This involves aggressively clock-gating functional units, flushing caches to non-volatile memory, and issuing a "last-gasp" warning to the operating system. The primary function is data preservation and prevention of catastrophic failure, rather than continued operation.

  stateDiagram-v2
  [] --> Normal_Operation
  Normal_Operation --> Predictive_Failure_Detection: Precursor event detected
  Predictive_Failure_Detection --> Graceful_Degradation: Fault imminent
  Graceful_Degradation --> Safe_Shutdown: Data flushed
  Safe_Shutdown --> []
  Normal_Operation --> Normal_Operation: No precursor

• Derivative 1.5.2: Fail-Safe Heuristic Fallback

  • Enabling Description: The DNN control unit includes a built-in self-test mechanism. If the DNN output becomes unstable or its inference latency exceeds a critical threshold (indicating a failure in the DNN itself), the control logic automatically bypasses the DNN. Control of the datapath reverts to a set of pre-programmed, conservative hardware heuristics (e.g., a simple static branch predictor, fixed clock frequency). This ensures that the processor remains functional, albeit at reduced efficiency, rather than crashing due to a fault in its advanced control system.

  graph TD
  A{Input Data} --> B{DNN Control};
  B --DNN OK?--> C{Generate DNN Control Signal};
  C --> E[Mux];
  A --> D{Generate Heuristic Control Signal};
  D --> E;
  E --> F[Datapath];
  B --DNN Failed--> D;
  ```

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Disclosure 2: Combination Prior Art with Open-Source Standards

• Scenario 2.1: RISC-V Custom Instruction for DNN Input

  • Enabling Description: The processor is based on the open-source RISC-V instruction set architecture (ISA). A custom instruction, dnn.input, is added to the ISA. This instruction allows software to directly push data (e.g., application-level metadata, sensor readings) into the input registers of the control unit's DNN. This creates a standardized, low-latency pathway for software to provide hints to the hardware control logic, enabling tighter co-design of software algorithms and hardware optimization. A compiler toolchain (e.g., based on LLVM) would be modified to automatically insert these instructions at critical junctures in the code.

• Scenario 2.2: DNN Control Integrated with Open-Source RTOS

  • Enabling Description: The DNN-controlled processor is managed by an open-source real-time operating system (RTOS), such as Zephyr or FreeRTOS. The RTOS scheduler is modified to be "DNN-aware." The DNN's predictions about future computational load are provided to the scheduler via a shared memory interface. The scheduler uses these predictions to proactively adjust task priorities and time slices, preventing deadline misses in hard real-time tasks by allocating resources before the high-load period begins, rather than reacting to it.

• Scenario 2.3: Federated Learning of DNN Controllers via gRPC

  • Enabling Description: A fleet of devices, each containing a DNN-controlled processor, participates in a federated learning scheme to collectively improve their control models without sharing raw data. The devices use the open-source gRPC framework for communication. Each device trains its local DNN model based on its specific workload. Periodically, it serializes the model weight updates (not the data) using Protocol Buffers and sends them to a central aggregation server via a secure gRPC stream. The server averages the updates and sends back a globally improved model. This allows the entire fleet to benefit from the operational experience of each individual unit.