## PRODUCT REQUIREMENTS DOCUMENT: Dynamic Loop Frequency Adjustment

This PRD outlines the requirements for implementing dynamic adjustment of the main loop frequency in AIVA, based on profiling data collected in GPM-030 (Loop Execution Time Profiling). The goal is to optimize AIVA's performance by adapting the loop frequency to the current workload, ensuring efficient resource utilization and responsiveness.

### GPM-031: Implement Basic Loop Frequency Control

**Goal:** Introduce a mechanism to manually adjust the main loop frequency.
**Acceptance Criteria:**
- A configuration parameter exists to set the target loop frequency (in Hz).
- A function exists to apply the configured loop frequency.
- The main loop utilizes this function to control its execution rate.
- Manually adjusting the configuration parameter changes the actual loop frequency, verifiable through external monitoring tools.
**Files:** `config.py`, `aiva_core.py`
**Spawns:** GPM-039 (Introduce a simple feedback loop based on collected data)

### GPM-032: Integrate Profiling Data into Frequency Adjustment

**Goal:** Feed execution time profiling data into the loop frequency control mechanism.
**Acceptance Criteria:**
- The loop frequency adjustment function receives the average execution time of the last N iterations (N is configurable).
- The average execution time is calculated using the profiling data collected in GPM-030.
- The value of N is configurable via `config.py`.
**Files:** `aiva_core.py`, `profiling.py`
**Spawns:** GPM-040 (Define initial adjustment strategies)

### GPM-033: Implement a Simple Proportional Adjustment Strategy

**Goal:** Implement a basic proportional control algorithm to adjust the loop frequency based on the execution time.
**Acceptance Criteria:**
- The loop frequency is adjusted proportionally to the difference between the target execution time (derived from the target frequency) and the measured average execution time.
- A configurable gain parameter determines the sensitivity of the adjustment.
- The proportional control algorithm is implemented in a dedicated function.
**Files:** `aiva_core.py`, `control.py`
**Spawns:** GPM-041 (Introduce limits to prevent runaway frequency adjustments)

### GPM-034: Implement Minimum and Maximum Frequency Limits

**Goal:** Prevent the loop frequency from exceeding safe operating limits.
**Acceptance Criteria:**
- Configuration parameters define the minimum and maximum allowed loop frequencies.
- The loop frequency adjustment function respects these limits, clamping the adjusted frequency within the allowed range.
- Alerts are logged if the adjustment algorithm attempts to exceed the frequency limits.
**Files:** `config.py`, `aiva_core.py`, `control.py`, `logging.py`
**Spawns:** GPM-042 (Implement damping to prevent oscillations)

### GPM-035: Implement a Damping Mechanism

**Goal:** Introduce damping to prevent oscillations in the loop frequency.
**Acceptance Criteria:**
- A damping factor is applied to the frequency adjustment, reducing the magnitude of changes based on the previous adjustment.
- The damping factor is configurable.
- The damping mechanism is integrated into the frequency adjustment function.
**Files:** `config.py`, `control.py`
**Spawns:** GPM-043 (Logging and visualization of Frequency Adjustments)

### GPM-036: Implement Logging of Frequency Adjustments

**Goal:** Log the loop frequency and execution time data for analysis and debugging.
**Acceptance Criteria:**
- The current loop frequency, average execution time, and adjustment parameters are logged periodically.
- The logging interval is configurable.
- Log entries include timestamps.
**Files:** `logging.py`, `aiva_core.py`
**Spawns:** GPM-044 (Implement different adjustment strategies selectable via configuration)

### GPM-037: Visualization of Frequency Adjustment Data

**Goal:** Create a simple visualization tool to monitor the loop frequency and execution time.
**Acceptance Criteria:**
- A script generates a graph showing the loop frequency and average execution time over time.
- The graph is updated periodically.
- The script can read log files generated in GPM-036.
**Files:** `visualization.py` (or use an existing visualization library)
**Spawns:** GPM-045 (A/B test different adjustment strategies and compare their performance)
