151 lines
6.8 KiB
TeX
151 lines
6.8 KiB
TeX
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% 07_embodiments.tex
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% Embodiments of the Invention
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% ===========================================
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\section{Embodiments}
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The following embodiments illustrate representative implementations of the
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adaptive virtual machine architecture described herein. These embodiments
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are provided for explanatory purposes only and should not be construed as
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limiting the scope of the invention. Numerous variations, combinations,
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and extensions will be apparent to those skilled in the art.
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\subsection{Embodiment A: Adaptive Interpreter in a Stack-Based VM}
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In one embodiment, the invention is integrated directly into a stack-based
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virtual machine that executes threaded code. The runtime maintains the state
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vector, updates execution heat on each word invocation, and applies temporal
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decay and entropy filtering in background cycles.
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The Jacquard Mode Selector evaluates metrics such as entropy slope, pipeline
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pressure, and variance to switch between baseline, temporal, inference-driven,
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and fully adaptive modes. Each mode configures the feedback loops (L1–L7)
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differently, allowing the interpreter to maintain stable and optimized
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execution across a wide variety of program structures, including tight loops,
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branch-heavy logic, and deeply nested control flows.
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\subsection{Embodiment B: Embedded Runtime for Constrained Systems}
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In another embodiment, the invention is deployed as a compact runtime for
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resource-limited devices such as microcontrollers, industrial controllers,
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or safety-critical embedded modules. The feedback-loop architecture is
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implemented using lightweight integer arithmetic, and the state vector is
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compressed to a minimal subset (e.g., heat, entropy window, stability score).
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The adaptive mode system allows the device to respond intelligently to
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fluctuating sensor inputs, sporadic interrupts, and mixed real-time workloads
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without requiring complex heuristics or manual tuning. Bounded adaptation
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ensures deterministic timing when required.
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\subsection{Embodiment C: Multi-Threaded Execution Engine}
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In an alternative embodiment, the invention is implemented within a
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multi-threaded execution engine. Each thread maintains its own state vector,
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while a coordinating controller aggregates selected global metrics
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(e.g., cross-thread stability, shared pipeline pressure) to inform mode
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selection.
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This embodiment is well-suited for high-performance computing contexts or
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runtime environments that execute concurrent workloads exhibiting different
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temporal or statistical characteristics.
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\newpage
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\subsection{Embodiment D: Just-In-Time (JIT) Compilation Environment}
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In some embodiments, the invention may be integrated with a JIT compiler.
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The Jacquard Mode Selector influences JIT policies such as:
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\begin{itemize}
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\item when to optimize hot paths,
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\item how aggressively to inline functions,
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\item whether to activate predictive prefetching,
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\item or when to adjust code generation strategies.
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\end{itemize}
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The feedback loops provide input signals derived from execution heat,
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entropy stability, or workload phase changes. This integration enables the
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compiler to target stable steady-state patterns while avoiding excessive
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oscillation in code generation.
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\subsection{Embodiment E: Adaptive Microkernel or Runtime Orchestrator}
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In another embodiment, the invention is incorporated into a microkernel or
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runtime orchestration layer responsible for managing low-level tasks,
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scheduling, and resource allocation. The state vector may include
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microkernel-specific signals such as:
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\begin{itemize}
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\item task execution periodicity,
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\item inter-process message timing,
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\item or virtualization overhead.
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\end{itemize}
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The adaptive modes allow the kernel to adjust scheduling heuristics,
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time-slicing strategies, and cache behavior based on observed workload
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conditions, enabling improved determinism and reduced latency.
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\subsection{Embodiment F: Statistical Inference-Driven Runtime}
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In certain embodiments, the inference loop (L2) and inference-weighting loop
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(L6) are given primary importance. The runtime employs statistical models—
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potentially including Bayesian estimators, weighted moving averages, or
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probabilistic classifiers—to predict upcoming workload shifts.
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This embodiment is particularly effective in environments where workloads
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exhibit repeated but non-uniform patterns, enabling the system to anticipate
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transitions and adjust modes proactively.
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\subsection{Embodiment G: Hybrid Adaptive System}
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A further embodiment combines two or more of the previously described
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approaches. For example, a stack-based interpreter may incorporate JIT-style
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optimizations only when the inference system determines that the workload has
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stabilized sufficiently. Alternatively, an embedded system may use a simplified
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state vector but rely on a kernel-level orchestration layer for global mode
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coordination.
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\subsection{Embodiment H: Simulation and Analysis Environment}
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In yet another embodiment, the invention is deployed as part of a simulation
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or analysis environment used to evaluate program behavior, runtime stability,
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or performance characteristics. The feedback loops and mode selector operate
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normally, but no direct program is executed; instead, synthetic or replayed
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workloads drive the system.
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This embodiment enables developers to observe the adaptive behavior of the
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system under controlled conditions, perform design space exploration, or
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validate workload-specific behaviors.
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\subsection{Embodiment I: Minimal-Loop Configuration}
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Certain embodiments implement a reduced set of loops—for instance, using only
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L1, L3, and L7—while still leveraging the Jacquard Mode Selector to control
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adaptive behavior. This embodiment may be used in systems where power,
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latency, or resource constraints preclude the use of more complex feedback
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structures, while still providing workload-aware adaptation.
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\subsection{Embodiment J: Distributed or Networked Runtime}
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In some embodiments, the invention is implemented in a distributed execution
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environment where multiple runtime nodes share or exchange selected metrics.
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Each node maintains its own state vector, but a global or partially shared
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controller may influence mode transitions to maintain stable behavior across
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the distributed system.
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This embodiment is suited for cloud, edge-compute, and multi-agent processing
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environments.
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\subsection{Summary of Embodiments}
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The embodiments described above demonstrate that the invention is applicable
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to a wide variety of execution environments, including interpreters, embedded
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systems, microkernels, JIT-enabled runtimes, and distributed execution engines.
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All such embodiments fall within the scope of the invention so long as they
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maintain a state vector, coordinate internal feedback loops, characterize
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workloads, and select or adjust execution modes in response to observed
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runtime conditions.
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\newpage
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