Architecture Intelligence Module Specification

Home / FrontierForge / Architecture Intelligence Module

Architecture Intelligence Module

The Architecture Intelligence Module Specification defines an open source framework for analyzing, modeling, comparing, and optimizing computing architectures across CPUs, GPUs, AI accelerators, and chiplet-based systems.

The specification provides a modular architecture analysis framework for examining processor structure, execution behavior, memory systems, interconnects, workload behavior, performance constraints, energy efficiency, and architectural bottlenecks.

The specification is designed to remain hardware vendor neutral and may be implemented across different processor architectures, accelerator architectures, operating systems, workloads, and analysis environments.

Core Architecture Analysis Module

The Core Architecture Analysis Module provides the foundational architecture analysis capabilities shared across supported computing systems.

Architecture Identification

Processor identification
Architecture family identification
Instruction set identification
Core and compute resource identification
Cache hierarchy identification
Memory subsystem identification
Interconnect identification
Accelerator identification
Chiplet topology identification
Hardware capability detection

Architecture Modeling

Architectural topology modeling
Execution resource modeling
Memory hierarchy modeling
Cache hierarchy modeling
Interconnect modeling
Compute resource modeling
Dependency modeling
Resource contention modeling
Latency modeling
Throughput modeling
Power modeling
Energy modeling

Workload Mapping

Workload characterization
Instruction distribution analysis
Compute intensity analysis
Memory intensity analysis
Parallelism analysis
Data movement analysis
Dependency analysis
Synchronization analysis
Resource utilization analysis
Workload-to-hardware mapping

CPU Analysis Module

The CPU Analysis Module evaluates processor architecture, instruction execution, memory behavior, pipeline efficiency, and processor bottlenecks.

Pipeline Analysis

Pipeline depth analysis
Pipeline stage utilization
Instruction throughput analysis
Instruction latency analysis
Dependency analysis
Pipeline stall detection
Pipeline hazard detection
Execution port analysis
Instruction scheduling analysis

Cache Analysis

Cache hierarchy analysis
Cache capacity analysis
Cache associativity analysis
Cache hit and miss analysis
Cache locality analysis
Cache contention analysis
Cache replacement behavior
Cache bandwidth analysis
Cache latency analysis

Branch Prediction Analysis

Branch frequency analysis
Branch prediction accuracy
Misprediction detection
Branch penalty analysis
Control flow analysis
Branch target behavior
Speculative execution analysis
Control flow bottleneck detection

Instruction Set Analysis

Instruction distribution analysis
Instruction frequency analysis
Instruction latency analysis
Instruction throughput analysis
Vector instruction analysis
SIMD utilization analysis
Specialized instruction analysis
ISA feature utilization
Instruction efficiency analysis

Memory Hierarchy Analysis

Register utilization
L1 cache analysis
L2 cache analysis
Last-level cache analysis
Main memory analysis
Memory bandwidth analysis
Memory latency analysis
Memory access pattern analysis
NUMA analysis
Data movement analysis

CPU Bottleneck Detection

Pipeline bottleneck detection
Execution bottleneck detection
Memory bottleneck detection
Cache bottleneck detection
Branch bottleneck detection
Instruction bottleneck detection
Synchronization bottleneck detection
Parallelism bottleneck detection
Resource contention detection
Composite bottleneck analysis

GPU Analysis Module

The GPU Analysis Module evaluates massively parallel architectures, compute resources, scheduling behavior, memory systems, and workload execution.

Compute Unit Analysis

Compute unit identification
Compute unit utilization
Arithmetic unit utilization
Vector processing analysis
Matrix processing analysis
Occupancy analysis
Register utilization
Shared memory utilization
Parallel execution analysis

Scheduling Analysis

Thread scheduling analysis
Warp or wavefront analysis
Workgroup analysis
Occupancy analysis
Scheduling efficiency
Synchronization analysis
Load balancing analysis
Execution divergence analysis
Scheduling bottleneck detection

Memory Subsystem Analysis

Global memory analysis
Cache analysis
Shared memory analysis
Local memory analysis
Memory bandwidth analysis
Memory latency analysis
Memory coalescing analysis
Memory contention analysis
Data movement analysis

Workload Optimization

Kernel analysis
Parallelism analysis
Occupancy optimization
Memory access optimization
Thread distribution optimization
Register optimization
Shared memory optimization
Workgroup optimization
Divergence reduction
Compute-to-memory optimization

AI Accelerator Analysis Module

The AI Accelerator Analysis Module evaluates specialized architectures designed for machine learning and neural network workloads.

Tensor Processing Analysis

Tensor processing analysis
Tensor core analysis
Matrix engine analysis
Vector engine analysis
Tensor operation analysis
Precision analysis
Quantization analysis
Sparsity analysis
Tensor utilization analysis
Dataflow analysis

Throughput Analysis

Operations-per-second analysis
Tensor throughput analysis
Inference throughput analysis
Training throughput analysis
Batch-size analysis
Latency-throughput analysis
Compute utilization analysis
Memory bandwidth analysis

Energy Efficiency Analysis

Energy-per-operation analysis
Energy-per-inference analysis
Power utilization analysis
Compute efficiency analysis
Memory energy analysis
Data movement energy analysis
Thermal efficiency analysis
Performance-per-watt analysis

Chiplet Analysis Module

The Chiplet Analysis Module evaluates systems composed of multiple interconnected dies, chiplets, accelerators, memory components, and heterogeneous processing elements.

Interconnect Analysis

Interconnect topology analysis
Interconnect bandwidth analysis
Link utilization analysis
Link latency analysis
Routing analysis
Congestion analysis
Protocol overhead analysis
Interconnect bottleneck detection

Chiplet Communication Evaluation

Chiplet-to-chiplet communication
Data transfer analysis
Communication frequency analysis
Communication bandwidth analysis
Communication latency analysis
Synchronization overhead
Coherency traffic analysis
Protocol overhead analysis
Data locality analysis

Latency Analysis

Die-to-die latency
Memory access latency
Interconnect latency
Queue latency
Synchronization latency
Communication latency
End-to-end workload latency
Latency variance analysis

Packaging Analysis

Chiplet arrangement analysis
Package topology analysis
Die placement analysis
Thermal considerations
Power delivery considerations
Interconnect density
Package bandwidth
Signal integrity considerations
Manufacturing complexity analysis
Packaging efficiency

Cross-Architecture Analysis Module

The Cross-Architecture Analysis Module provides standardized analysis and comparison between different computing architectures.

Architecture Comparison

CPU versus GPU analysis
CPU versus AI accelerator analysis
GPU versus AI accelerator analysis
Monolithic versus chiplet architecture analysis
Homogeneous versus heterogeneous architecture analysis
General-purpose versus specialized architecture analysis

Workload Suitability

Workload compatibility analysis
Architecture suitability scoring
Performance estimation
Energy efficiency comparison
Memory efficiency comparison
Parallelism suitability
Latency suitability
Throughput suitability
Resource suitability

Bottleneck Correlation

Cross-component bottleneck analysis
System-level bottleneck propagation
Bottleneck dependency mapping
Bottleneck severity scoring
Bottleneck prioritization
Optimization impact analysis

Performance Analysis Module

The Performance Analysis Module establishes standardized methods for measuring and interpreting architectural performance.

Performance Metrics

Execution time
Instruction throughput
Operations per second
Memory bandwidth
Memory latency
Cache hit rate
Branch prediction accuracy
Compute utilization
Accelerator utilization
Interconnect utilization
Workload throughput
Workload latency

Efficiency Metrics

Instructions per cycle
Operations per cycle
Compute efficiency
Memory efficiency
Parallel efficiency
Accelerator efficiency
Performance per watt
Energy per operation
Energy per workload
Resource utilization

Performance Correlation

Hardware-to-workload correlation
Metric correlation
Resource-to-performance correlation
Bottleneck-to-performance correlation
Performance regression detection
Performance scaling analysis

Bottleneck Intelligence Module

The Bottleneck Intelligence Module identifies architectural constraints and determines their effect on workload execution.

Bottleneck Classification

Compute bottlenecks
Memory bottlenecks
Cache bottlenecks
Pipeline bottlenecks
Branch bottlenecks
Scheduling bottlenecks
Synchronization bottlenecks
Interconnect bottlenecks
Communication bottlenecks
Thermal bottlenecks
Power bottlenecks

Bottleneck Prioritization

Bottleneck severity scoring
Performance impact estimation
Energy impact estimation
Workload impact estimation
Resource impact estimation
Bottleneck dependency analysis
Bottleneck prioritization
Optimization priority ranking

Bottleneck Root Cause Analysis

Root cause identification
Contributing factor analysis
Bottleneck propagation analysis
Bottleneck dependency mapping
Constraint isolation
Secondary bottleneck detection
Bottleneck interaction analysis

Optimization Intelligence Module

The Optimization Intelligence Module converts architecture analysis into actionable optimization recommendations.

Optimization Recommendations

Instruction optimization
Pipeline optimization
Cache optimization
Memory optimization
Branch optimization
Parallelization recommendations
Scheduling recommendations
Kernel optimization
Tensor optimization
Data movement optimization
Interconnect optimization
Workload placement optimization

Optimization Modeling

Expected performance improvement
Expected energy improvement
Resource requirements
Trade-off analysis
Optimization confidence
Regression risk
Architecture-specific recommendations
Workload-specific recommendations

Architecture Simulation Module

The Architecture Simulation Module provides mechanisms for evaluating architectural behavior when direct hardware measurements are unavailable.

Simulation Capabilities

Pipeline simulation
Cache simulation
Memory simulation
Branch prediction simulation
Instruction execution simulation
GPU scheduling simulation
Accelerator execution simulation
Interconnect simulation
Chiplet communication simulation

Predictive Analysis

Performance prediction
Latency prediction
Throughput prediction
Resource utilization prediction
Energy prediction
Bottleneck prediction
Scaling analysis
What-if architecture analysis

Hardware Telemetry Module

The Hardware Telemetry Module collects and processes hardware performance information from supported systems.

Telemetry Sources

Processor performance counters
Performance monitoring units
Memory controllers
Cache counters
GPU telemetry
Accelerator telemetry
Thermal sensors
Power measurements
Interconnect telemetry
Operating system performance data

Telemetry Processing

Metric normalization
Sampling
Aggregation
Time-series analysis
Event correlation
Anomaly detection
Workload correlation
Measurement validation

Architecture Knowledge Module

The Architecture Knowledge Module maintains structured information about processor, accelerator, memory, interconnect, and chiplet architectures.

Architecture Records

Architecture specifications
Instruction sets
Microarchitectural characteristics
Cache configurations
Compute resources
Memory systems
Accelerator capabilities
Interconnect capabilities
Chiplet configurations
Packaging characteristics

Knowledge Relationships

Architecture-to-workload relationships
Component dependencies
Performance relationships
Bottleneck relationships
Optimization relationships
Hardware capability relationships

Reporting Module

The Reporting Module produces standardized architecture analysis results.

Reports

Architecture overview
CPU analysis report
GPU analysis report
AI accelerator report
Chiplet analysis report
Performance report
Bottleneck report
Energy efficiency report
Optimization report
Cross-architecture comparison report
Architecture simulation report

Visualization

Architecture topology
Pipeline visualization
Cache hierarchy visualization
Memory hierarchy visualization
Compute utilization
Bottleneck maps
Data movement maps
Interconnect maps
Chiplet topology
Workload execution profiles
Performance profiles

Provenance and Reproducibility Module

The Provenance and Reproducibility Module records the sources, configurations, measurements, models, assumptions, and analytical processes used to produce architecture analysis results.

Provenance Tracking

Hardware identification
Architecture identification
Software environment
Benchmark identification
Workload identification
Measurement source
Measurement timestamp
Configuration parameters
Analysis methodology
Simulation parameters
Model version
Result provenance

Reproducibility

Reproducible analysis configurations
Repeatable measurements
Versioned analysis models
Deterministic simulation modes where practical
Comparable benchmark configurations
Result validation

Evidence and Confidence Module

The Evidence and Confidence Module distinguishes measured observations from derived conclusions and modeled predictions.

Evidence Classification

Direct measurement
Hardware counter measurement
Benchmark measurement
Simulation result
Derived metric
Model-based estimate
Heuristic inference
Expert interpretation

Confidence Analysis

Confidence scoring
Evidence quality assessment
Measurement reliability
Model reliability
Assumption tracking
Uncertainty identification
Result qualification

Architecture Security Module

The Architecture Security Module protects architecture analysis data, telemetry, configurations, and analytical results.

Security Requirements

Access control
Data integrity verification
Analysis provenance
Configuration integrity
Plugin isolation
Secure telemetry handling
Result integrity
Audit logging

Sensitive Analysis Protection

Implementations SHOULD provide mechanisms for protecting proprietary architecture information, confidential workloads, hardware telemetry, benchmark results, and internal performance analysis.

Standard Analysis Model

Implementations SHOULD represent architecture analysis using a standardized analysis model containing:

Hardware identity
Architecture identity
Architecture components
Workload identity
Analysis type
Measurement data
Derived metrics
Detected bottlenecks
Root causes
Confidence levels
Optimization recommendations
Provenance information
Analysis timestamp
Model versions

Modular Analysis

The specification is designed as a modular system.

Core modules provide the fundamental architecture analysis capabilities.

Optional modules MAY extend the system with additional hardware types, analysis methods, telemetry sources, simulation capabilities, optimization methods, and reporting capabilities.

Modules SHOULD communicate through standardized data models and interfaces.

A module SHOULD be independently replaceable without requiring changes to unrelated modules.

Implementations SHOULD allow individual modules to be enabled, disabled, replaced, or extended according to deployment requirements.

Optional Plugin Modules

The specification supports optional plugins for specialized architecture analysis.

CPU Architecture Plugins

Processor family analysis
ISA-specific analysis
Microarchitecture analysis
Performance counter integration
Pipeline modeling
Cache modeling

GPU Architecture Plugins

GPU architecture analysis
Compute unit analysis
Scheduling analysis
Kernel analysis
GPU memory analysis
GPU telemetry integration

AI Accelerator Plugins

Neural processing unit analysis
Tensor processor analysis
Matrix accelerator analysis
Neural network workload analysis
Accelerator telemetry
Model optimization

Chiplet Plugins

Chiplet topology analysis
Die-to-die communication analysis
Interconnect analysis
Package analysis
Heterogeneous architecture analysis

FPGA Analysis Plugins

FPGA resource analysis
Logic utilization analysis
DSP utilization analysis
Block RAM analysis
Routing analysis
FPGA workload optimization

Memory Architecture Plugins

DRAM analysis
HBM analysis
SRAM analysis
Persistent memory analysis
Memory controller analysis
Memory bandwidth analysis

Interconnect Plugins

Die-to-die interconnect analysis
System interconnect analysis
Network-on-chip analysis
Memory interconnect analysis
Fabric analysis
Interconnect congestion analysis

Benchmark Plugins

Benchmark integration
Workload characterization
Benchmark normalization
Benchmark comparison
Benchmark reproducibility
Benchmark result validation

Compiler Analysis Plugins

Compiler optimization analysis
Generated instruction analysis
Vectorization analysis
Optimization pass analysis
Code generation analysis
Compiler-to-architecture correlation

Power and Thermal Plugins

Power analysis
Energy analysis
Thermal analysis
Thermal throttling detection
Performance-per-watt analysis
Energy optimization

Simulation Plugins

Microarchitecture simulation
CPU simulation
GPU simulation
Accelerator simulation
Memory simulation
Interconnect simulation
Chiplet simulation

Vendor Neutrality

The specification is vendor neutral and SHOULD support analysis across competing hardware architectures.

Implementations SHOULD:

Support multiple hardware architectures
Avoid vendor lock-in
Normalize architecture terminology
Separate measurement from interpretation
Identify analytical assumptions
Preserve raw measurements
Provide reproducible analysis
Support common comparison metrics

Vendor-specific plugins MAY provide specialized functionality without making vendor-specific dependencies mandatory for the core specification.

Human Analysis and Validation

Architecture analysis SHOULD distinguish between measured results, modeled results, inferred results, and recommendations.

Significant analytical conclusions SHOULD identify their evidence class and confidence level.

Implementations SHOULD allow human analysts to review, override, annotate, validate, or reject automated conclusions.

Automated optimization recommendations SHOULD remain distinguishable from measured architectural behavior.

Specification Branding License (SBL)

Standard

Fully AGPL-3.0+ compliant system
Copyleft enforced for network deployments
Required attribution:
- Roxanne Ardary
- https://www.roxanneardary.com/

Optional

Specification Branding License (SBL)
Attribution-free commercial deployment
Pricing based on scale, usage, and deployment scope
https://roxanneardary.com/architecture-intelligence-module/

License & Notice Requirements

Architecture Intelligence Module Specification is released under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).

By contributing to any Open Arsenal project, you agree that your contributions will also be released under this license.

Please note the following:

All contributions must comply with the AGPL-3.0+ terms.
Under Section 7 of the license, all redistributions, forks, and derivative works must preserve attribution to: Roxanne Ardary and roxanneardary.com.
Architecture Intelligence Module Specification specifications are free to use with attribution. A Specification Branding License can be negotiated upon request.
The project’s notice.md file tracks attribution requirements and contributor acknowledgments. Any update that adds new contributors or modifies attribution should also update notice.md.
When submitting a pull request, ensure that any new files maintain the attribution headers where applicable.
Network-deployed versions of this software must also remain fully AGPL-3.0+ compliant, including exposure of source code modifications when applicable under the license.

For full legal details, please refer to the AGPL-3.0+ license and the project’s notice.md file.

Notice – FrontierForge

Attribution Requirement: Under Section 7 of the AGPL 3.0+ license, all redistributions, forks, and derivative works, including network-deployed versions of this project, must provide attribution to Roxanne Ardary and roxanneardary.com.

Contributors

This file tracks contributors and their specific contributions to the project.

Roxanne Ardary, roxanneardary.com – June 8, 2026
Created the repository for FrontierForge. Designed the project vision, architecture, and feature roadmap for an AGPL 3.0+ open source AI platform focused on scientific research, invention, semiconductor innovation, engineering design, simulation, and knowledge discovery.
[Add other contributors here] – [Date]
[Describe contribution in one sentence]

License – FrontierForge

This repository is licensed under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).

Key Points:

You are free to use, modify, and distribute the code.
All redistributions, forks, and derivative works or network-deployed versions must also be licensed under AGPL-3.0+ and provide attribution to Roxanne Ardary and roxanneardary.com as required under Section 7 of the license.
The software is provided “as is,” without warranty of any kind.

For the full license text, see GNU AGPL-3.0 License: https://www.gnu.org/licenses/agpl-3.0.html