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Product Requirements Document

1. Product Overview

Product Vision

AIHW-Bench will become a leading open-source AI infrastructure tool for evaluating AI workloads across CPUs, GPUs, embedded platforms, RTL simulators, FPGA prototypes, ASIC accelerators, and future custom silicon.

The product vision is to make hardware evaluation reproducible, comparable, extensible, and approachable without hiding the engineering details that make benchmark results trustworthy.

Product Mission

AIHW-Bench helps engineers and researchers answer a hard question with confidence:

How does this AI workload actually perform on this hardware or execution environment, and can someone else reproduce the result?

Elevator Pitch

AIHW-Bench is a universal benchmarking and profiling framework for AI hardware. It gives ML engineers, hardware teams, researchers, and accelerator vendors one consistent CLI, Python API, plugin model, metrics system, and reporting pipeline for comparing AI workloads across software runtimes and hardware targets.

Problem Statement

AI hardware benchmarking is fragmented. Teams use different scripts, runtime-specific benchmark tools, profiler outputs, spreadsheet templates, and undocumented assumptions. Results are often hard to reproduce, hard to compare, and hard to extend to new platforms such as RTL simulators, FPGA boards, embedded devices, or custom accelerators.

Existing tools are strong in their own domains, but they rarely provide one product surface for:

  • AI model runtime benchmarking.
  • Hardware and simulator abstraction.
  • Extensible metrics.
  • Reproducible session storage.
  • Rich reports and comparisons.
  • Plugin-driven third-party ecosystem support.
  • Release-quality open-source packaging.

Opportunity

The AI hardware ecosystem is expanding quickly. Teams need a neutral tool that can benchmark today's mainstream runtimes and tomorrow's specialized hardware. AIHW-Bench can become the common evaluation layer between ML infrastructure, hardware architecture, compiler development, embedded AI, and accelerator vendor ecosystems.

Product Scope

In scope:

  • Benchmark orchestration.
  • Profiling orchestration.
  • Metrics and statistics.
  • Hardware abstraction.
  • Runtime backend adapters.
  • Plugin system.
  • Configuration system.
  • Session persistence.
  • Reports and exports.
  • Visualizations.
  • CLI and Python API.
  • Documentation and examples.
  • Release automation and packaging.
  • Open-source community workflows.

Out of Scope

Out of scope for the core product:

  • Training models.
  • Hosting a proprietary benchmark leaderboard.
  • Redistributing vendor SDKs, closed simulator binaries, or licensed models.
  • Certifying vendor claims.
  • Running untrusted plugin code in a hard security sandbox in the initial product.
  • Providing a hosted SaaS service as part of the core package.

Success Criteria

AIHW-Bench is successful when:

  • New users can install it and run a first benchmark in under 10 minutes.
  • Benchmark sessions include enough metadata to reproduce results.
  • Plugin authors can add backends without changing the core codebase.
  • Reports are useful to engineers, researchers, and maintainers.
  • Releases are automated, signed or checksum-backed, and multi-platform.
  • Documentation is clear enough for independent implementation and extension work.
  • The project earns adoption across ML, hardware, embedded, compiler, and research communities.

2. Target Users

ML Engineers

Goals:

  • Compare model performance across CPUs, GPUs, and deployment runtimes.
  • Understand latency, throughput, memory, and batch-size tradeoffs.
  • Generate reports for deployment decisions.

Pain points:

  • Runtime-specific benchmark scripts.
  • Inconsistent metrics.
  • Missing reproducibility metadata.

Expected workflow:

  1. Install AIHW-Bench.
  2. Select a model, backend, device, precision, and batch size.
  3. Run benchmarks.
  4. Compare sessions.
  5. Export reports for reviews.

AI Researchers

Goals:

  • Publish reproducible benchmark data.
  • Compare models across hardware and precision modes.
  • Share benchmark artifacts with papers and repositories.

Pain points:

  • Ad hoc scripts are difficult to reproduce.
  • Hardware details are often missing from published results.
  • Report formats vary across projects.

Expected workflow:

  1. Define benchmark configuration.
  2. Run repeatable experiments.
  3. Store session artifacts.
  4. Export Markdown, JSON, and citation-friendly reports.

FPGA Engineers

Goals:

  • Benchmark accelerator prototypes.
  • Compare FPGA execution against CPU, GPU, or simulator baselines.
  • Track performance across bitstream or design revisions.

Pain points:

  • Vendor tooling is siloed.
  • Board setup metadata is hard to preserve.
  • Latency measurements may mix transfer and execution time.

Expected workflow:

  1. Install core package and FPGA plugin.
  2. Register board or remote target.
  3. Run benchmark profile.
  4. Compare against historical sessions.

ASIC Engineers

Goals:

  • Evaluate pre-silicon and post-silicon performance.
  • Track design regressions.
  • Compare simulator and silicon behavior.

Pain points:

  • Simulator throughput is slow.
  • Toolchains differ across design stages.
  • Metrics require careful provenance.

Expected workflow:

  1. Configure simulator or silicon backend.
  2. Run targeted workloads.
  3. Capture timing, utilization, and metadata.
  4. Export design-review reports.

RTL Designers

Goals:

  • Benchmark RTL simulation outputs.
  • Compare microarchitecture changes.
  • Preserve simulator, waveform, and configuration metadata.

Pain points:

  • RTL results are often disconnected from model-level workloads.
  • Simulation runs are expensive and fragile.
  • Data extraction differs by simulator.

Expected workflow:

  1. Use simulator plugin.
  2. Execute or import run artifacts.
  3. Normalize metrics.
  4. Compare against baselines.

Embedded Engineers

Goals:

  • Measure AI workloads on constrained devices.
  • Capture memory, thermal, and power context.
  • Compare deployment formats such as TFLite or ONNX.

Pain points:

  • Device connectivity is unreliable.
  • Resource limits affect measurement quality.
  • Cross-compilation and runtime setup vary by target.

Expected workflow:

  1. Register embedded target.
  2. Run remote benchmark.
  3. Collect device telemetry.
  4. Generate deployment report.

Compiler Engineers

Goals:

  • Compare compiler optimizations.
  • Detect performance regressions.
  • Benchmark graph lowering and runtime kernels.

Pain points:

  • Existing tools do not align compiler metadata with model and hardware data.
  • Regression detection is often custom.
  • Benchmark inputs are not always controlled.

Expected workflow:

  1. Define compiler/runtime backend.
  2. Run benchmark matrix.
  3. Compare sessions by commit, optimization level, and target.
  4. Export regression reports.

Computer Architecture Researchers

Goals:

  • Evaluate architectural ideas.
  • Compare simulation and hardware measurements.
  • Publish transparent benchmark data.

Pain points:

  • Research simulators need custom integration.
  • Metrics require assumptions.
  • Reproducibility is difficult over long paper timelines.

Expected workflow:

  1. Implement plugin for simulator or architecture model.
  2. Run benchmark campaigns.
  3. Store raw observations and assumptions.
  4. Publish artifacts.

Students

Goals:

  • Learn AI hardware benchmarking.
  • Compare runtimes locally.
  • Understand metrics and tradeoffs.

Pain points:

  • Existing tools are scattered and advanced.
  • Setup can be intimidating.
  • Reports are not beginner-friendly.

Expected workflow:

  1. Install package.
  2. Run quick-start benchmark.
  3. Read generated report.
  4. Modify configuration.

Universities

Goals:

  • Teach reproducible performance evaluation.
  • Provide lab assignments.
  • Support research groups.

Pain points:

  • Lab environments are diverse.
  • Students need consistent tooling.
  • Research artifacts need durability.

Expected workflow:

  1. Package AIHW-Bench in course or lab environments.
  2. Provide standard configs.
  3. Collect reports.
  4. Compare submissions or experiments.

Open Source Contributors

Goals:

  • Add backends, metrics, reports, and documentation.
  • Improve quality and ecosystem support.
  • Maintain stable extension interfaces.

Pain points:

  • Unclear project boundaries.
  • Weak contribution workflows.
  • Hard-to-test plugin contracts.

Expected workflow:

  1. Read contributor guide and architecture docs.
  2. Open issue or proposal.
  3. Submit tested, documented PR.
  4. Participate in review.

3. Competitor Analysis

TensorBoard

Strengths:

  • Mature visualization ecosystem.
  • Familiar to ML practitioners.
  • Strong experiment tracking integrations.

Weaknesses:

  • Focused on model training and experiment visualization.
  • Not a hardware benchmark framework.
  • Does not provide hardware backend plugin contracts.

AIHW-Bench should do better:

  • Provide benchmark-specific sessions, metrics, comparisons, and hardware metadata.
  • Support hardware and simulator abstraction directly.

MLPerf

Strengths:

  • Industry-recognized benchmark suites.
  • Strong rules for benchmark credibility.
  • Broad hardware vendor participation.

Weaknesses:

  • Heavyweight process.
  • Narrower benchmark definitions.
  • Less focused on everyday developer workflows and arbitrary plugins.

AIHW-Bench should do better:

  • Offer lightweight local workflows while preserving reproducibility.
  • Support custom workloads and extension points.

PyTorch Benchmark

Strengths:

  • Deep PyTorch integration.
  • Useful for PyTorch operator and model performance.
  • Familiar to PyTorch developers.

Weaknesses:

  • Runtime-specific.
  • Limited hardware abstraction outside PyTorch execution.
  • Not designed as a universal plugin ecosystem.

AIHW-Bench should do better:

  • Benchmark PyTorch alongside ONNX Runtime, TensorFlow Lite, simulators, and custom accelerators.

ONNX Runtime Benchmark

Strengths:

  • Strong ONNX Runtime execution provider coverage.
  • Useful for model deployment evaluation.
  • Mature runtime diagnostics.

Weaknesses:

  • Runtime-specific.
  • Limited broader reporting and plugin architecture.
  • Does not cover non-ONNX execution environments.

AIHW-Bench should do better:

  • Normalize ONNX Runtime results with other backends and reports.

pytest-benchmark

Strengths:

  • Simple Python benchmarking inside tests.
  • Useful regression workflows.
  • Integrates with pytest.

Weaknesses:

  • Not focused on AI workloads or hardware metadata.
  • Not a full profiling and reporting system.
  • Not designed for accelerator plugins.

AIHW-Bench should do better:

  • Provide AI-specific metrics, hardware abstraction, and report artifacts.

perf

Strengths:

  • Powerful Linux performance analysis.
  • Low-level system counters.
  • Mature and scriptable.

Weaknesses:

  • Linux-specific.
  • Requires expertise.
  • Not AI model or multi-backend oriented.

AIHW-Bench should do better:

  • Provide a higher-level AI benchmark workflow and optionally integrate low-level profiling data.

hyperfine

Strengths:

  • Excellent CLI benchmarking simplicity.
  • Good statistical summaries.
  • Easy to use.

Weaknesses:

  • Process command benchmarking only.
  • No AI model, hardware, plugin, or profiling model.
  • Limited structured report semantics for AI workloads.

AIHW-Bench should do better:

  • Preserve hyperfine-like usability while adding AI/hardware semantics.

Intel VTune

Strengths:

  • Deep CPU profiling.
  • Rich performance analysis.
  • Production-grade tooling.

Weaknesses:

  • Vendor-specific.
  • Heavyweight.
  • Not a universal open-source benchmark framework.

AIHW-Bench should do better:

  • Offer vendor-neutral benchmarking and optionally ingest vendor profiler outputs.

NVIDIA Nsight

Strengths:

  • Deep GPU profiling.
  • Excellent CUDA analysis.
  • Mature visualization and diagnostics.

Weaknesses:

  • NVIDIA-specific.
  • Not a cross-hardware benchmark framework.
  • Requires specialized expertise.

AIHW-Bench should do better:

  • Normalize GPU insights with CPU, embedded, simulator, and accelerator results.

Google Benchmark

Strengths:

  • Mature C++ microbenchmarking.
  • Strong statistical rigor.
  • Widely adopted.

Weaknesses:

  • C++ focused.
  • Not AI model or hardware-lab oriented.
  • No Python-first CLI/report/plugin workflow.

AIHW-Bench should do better:

  • Bring benchmark rigor to AI hardware workflows with Python packaging and plugins.

4. Core Features

Essential

  • CLI with benchmark, profile, compare, report, export, doctor, version.
  • Python API for programmatic benchmarking.
  • Configuration from YAML, JSON, environment variables, and CLI.
  • Benchmark session model.
  • CPU-safe reference backend.
  • Metrics for latency, throughput, execution time, memory, batch size, precision, and system information.
  • Filesystem session storage.
  • JSON, CSV, Markdown, and HTML reports.
  • Plugin discovery and validation.
  • Documentation site.
  • Automated CI and release pipeline.

Advanced

  • PyTorch backend.
  • ONNX Runtime backend.
  • TensorFlow Lite backend.
  • GPU utilization profiling.
  • Historical comparison.
  • Interactive Plotly reports.
  • Regression thresholds.
  • Derived FLOPS, MACs, and arithmetic intensity.
  • Remote embedded execution.
  • Simulator integration contracts.
  • Plugin-provided CLI commands.

Future

  • Local web dashboard.
  • PDF reports.
  • Distributed benchmark scheduler.
  • Hosted benchmark database integration.
  • Power and thermal metrics.
  • Vendor profiler ingestion.
  • Signed plugin metadata.
  • Reproducibility bundles.

Experimental

  • RTL simulator plugins.
  • FPGA board orchestration.
  • ASIC lab automation adapters.
  • Compiler pass benchmarking.
  • Live benchmark telemetry.
  • Benchmark result attestation.

5. User Stories

  1. As an ML engineer, I want to benchmark a PyTorch model so that I can choose a deployment device.
  2. As an ML engineer, I want to compare ONNX Runtime and PyTorch so that I can select the faster runtime.
  3. As an ML engineer, I want batch-size sweeps so that I can understand throughput and latency tradeoffs.
  4. As an ML engineer, I want precision comparisons so that I can evaluate FP32, FP16, INT8, and future formats.
  5. As an ML engineer, I want JSON output so that I can automate benchmark analysis.
  6. As an ML engineer, I want Markdown reports so that I can paste results into pull requests.
  7. As an ML engineer, I want HTML reports so that I can share results with stakeholders.
  8. As an ML engineer, I want memory metrics so that I can detect deployment limits.
  9. As an ML engineer, I want regression thresholds so that CI can block performance regressions.
  10. As an ML engineer, I want a quick benchmark profile so that I can get a fast signal during development.
  11. As an AI researcher, I want reproducible configuration files so that published results can be rerun.
  12. As an AI researcher, I want hardware metadata captured automatically so that papers include accurate context.
  13. As an AI researcher, I want raw observations stored so that statistics can be audited.
  14. As an AI researcher, I want model metadata stored so that benchmark claims are traceable.
  15. As an AI researcher, I want citation metadata so that I can cite the tool properly.
  16. As an AI researcher, I want comparison reports so that I can evaluate model families.
  17. As an AI researcher, I want exported CSV files so that I can analyze results externally.
  18. As an AI researcher, I want session IDs so that experiments can be referenced reliably.
  19. As an AI researcher, I want documented metric assumptions so that derived metrics are transparent.
  20. As an AI researcher, I want versioned schemas so that old benchmark data remains readable.
  21. As an FPGA engineer, I want to register an FPGA backend so that benchmarks can run on my board.
  22. As an FPGA engineer, I want transfer time separated from compute time so that bottlenecks are visible.
  23. As an FPGA engineer, I want board metadata recorded so that bitstream results are reproducible.
  24. As an FPGA engineer, I want to compare bitstream revisions so that I can catch regressions.
  25. As an FPGA engineer, I want plugin diagnostics so that setup issues are easy to resolve.
  26. As an FPGA engineer, I want remote target support so that lab boards can be used from CI.
  27. As an FPGA engineer, I want utilization metrics so that I can understand resource efficiency.
  28. As an FPGA engineer, I want configurable warmup so that board initialization does not pollute results.
  29. As an FPGA engineer, I want custom metrics so that domain-specific counters can be reported.
  30. As an FPGA engineer, I want HTML comparison reports so that design reviews are easier.
  31. As an ASIC engineer, I want simulator backends so that pre-silicon results can be benchmarked.
  32. As an ASIC engineer, I want silicon and simulator sessions compared so that model drift is visible.
  33. As an ASIC engineer, I want execution metadata so that run conditions are preserved.
  34. As an ASIC engineer, I want failed runs stored so that debug evidence is not lost.
  35. As an ASIC engineer, I want long-running benchmark recovery so that expensive sessions remain inspectable.
  36. As an ASIC engineer, I want artifact checksums so that release-grade results are verifiable.
  37. As an ASIC engineer, I want custom report generators so that internal review formats can be supported.
  38. As an ASIC engineer, I want plugin API versioning so that vendor integrations remain stable.
  39. As an ASIC engineer, I want benchmark campaigns so that multiple workloads can be evaluated consistently.
  40. As an ASIC engineer, I want secure file handling so that lab paths are not accidentally exposed.
  41. As an RTL designer, I want to import simulator logs so that I can normalize results.
  42. As an RTL designer, I want waveform artifact references so that debug data is connected to metrics.
  43. As an RTL designer, I want low-iteration benchmark support so that slow simulations are practical.
  44. As an RTL designer, I want configuration profiles so that simulation settings can be reused.
  45. As an RTL designer, I want diagnostic warnings so that incomplete metrics are obvious.
  46. As an RTL designer, I want raw timing preserved so that custom analysis is possible.
  47. As an RTL designer, I want benchmark status states so that partial results are not confused with success.
  48. As an RTL designer, I want backend capability checks so that unsupported workloads fail early.
  49. As an RTL designer, I want comparison thresholds so that microarchitecture changes can be reviewed.
  50. As an RTL designer, I want report sections for assumptions so that simulator limitations are clear.
  51. As an embedded engineer, I want TensorFlow Lite support so that I can benchmark edge workloads.
  52. As an embedded engineer, I want device memory metrics so that I can avoid out-of-memory deployments.
  53. As an embedded engineer, I want thermal metadata so that performance throttling is visible.
  54. As an embedded engineer, I want offline reports so that results can be shared from lab machines.
  55. As an embedded engineer, I want remote execution so that constrained devices can be benchmarked safely.
  56. As an embedded engineer, I want installation diagnostics so that missing runtime dependencies are clear.
  57. As an embedded engineer, I want environment-variable configuration so that CI can run benchmarks.
  58. As an embedded engineer, I want lightweight installation so that devices do not require heavy packages.
  59. As an embedded engineer, I want artifact manifests so that generated files are organized.
  60. As an embedded engineer, I want profile summaries so that bottlenecks are visible.
  61. As a compiler engineer, I want to benchmark compiler output so that optimization impact is measurable.
  62. As a compiler engineer, I want backend metadata so that compiler flags are preserved.
  63. As a compiler engineer, I want session comparison by commit so that regressions are traceable.
  64. As a compiler engineer, I want machine-readable exports so that CI can analyze results.
  65. As a compiler engineer, I want benchmark matrices so that targets and optimization levels can be swept.
  66. As a compiler engineer, I want plugin-defined workload loaders so that custom IR can be supported.
  67. As a compiler engineer, I want latency distributions so that unstable optimizations are visible.
  68. As a compiler engineer, I want exact dependency versions so that toolchain changes are captured.
  69. As a compiler engineer, I want failure classification so that compiler and runtime errors are distinct.
  70. As a compiler engineer, I want minimal measurement overhead so that small kernels can be measured.
  71. As a computer architecture researcher, I want roofline charts so that hardware balance can be analyzed.
  72. As a computer architecture researcher, I want arithmetic intensity metrics so that workload behavior is clear.
  73. As a computer architecture researcher, I want simulator plugin support so that research tools can integrate.
  74. As a computer architecture researcher, I want assumptions stored with estimates so that papers remain honest.
  75. As a computer architecture researcher, I want reproducibility bundles so that artifacts can be archived.
  76. As a computer architecture researcher, I want custom visualization modules so that new analysis can be explored.
  77. As a computer architecture researcher, I want schema evolution so that long studies remain usable.
  78. As a computer architecture researcher, I want benchmark campaigns so that many models can be compared.
  79. As a computer architecture researcher, I want extensible hardware info models so that new architectures fit.
  80. As a computer architecture researcher, I want data export so that results can feed external tools.
  81. As a student, I want a simple quick start so that I can run my first benchmark easily.
  82. As a student, I want clear metric explanations so that I understand results.
  83. As a student, I want example configs so that I can learn by modifying working files.
  84. As a student, I want friendly CLI errors so that setup mistakes are easy to fix.
  85. As a student, I want lightweight local benchmarks so that I can use a laptop.
  86. As a university instructor, I want reproducible lab configs so that students run the same benchmark.
  87. As a university instructor, I want CSV exports so that assignments can be graded or analyzed.
  88. As a university instructor, I want platform-portable installation so that labs support multiple OSes.
  89. As a university instructor, I want documentation pages so that course material can reference stable concepts.
  90. As a university instructor, I want versioned releases so that semester materials do not drift.
  91. As an open-source contributor, I want clear architecture docs so that I can make changes safely.
  92. As an open-source contributor, I want plugin examples so that I can add integrations.
  93. As an open-source contributor, I want issue templates so that bug reports are actionable.
  94. As an open-source contributor, I want PR checklists so that quality expectations are clear.
  95. As an open-source contributor, I want typed public APIs so that integrations are reliable.
  96. As a maintainer, I want automated releases so that packaging is reproducible.
  97. As a maintainer, I want security scans so that dependency risk is visible.
  98. As a maintainer, I want SBOM artifacts so that releases support supply-chain review.
  99. As a maintainer, I want docs deployed automatically so that users see current guidance.
  100. As a maintainer, I want cross-platform installation verification so that releases do not break users.
  101. As an accelerator vendor, I want plugin branding metadata so that users know which plugin provides a backend.
  102. As an accelerator vendor, I want capability discovery so that unsupported models fail before execution.
  103. As an accelerator vendor, I want private metadata namespaces so that vendor-specific details can be preserved.
  104. As a DevOps engineer, I want Docker images so that benchmarks can run in controlled environments.
  105. As a DevOps engineer, I want GitHub Actions examples so that benchmark checks can run in CI.

6. Functional Requirements

Benchmark Engine

  • The product shall execute benchmark sessions from CLI and Python API.
  • The product shall support warmup, measured iterations, timeout, retries, cleanup, and recovery.
  • The product shall store raw observations before metric aggregation.
  • The product shall classify failed, partial, cancelled, and successful runs.

Metrics Engine

  • The product shall compute latency, throughput, FPS, execution time, warmup time, memory, utilization, batch size, precision, model size, MACs, estimated FLOPS, arithmetic intensity, hardware information, and system information.
  • The product shall include units and assumptions with every metric.
  • The product shall allow plugin-provided metrics.

Hardware Layer

  • The product shall model host and target hardware separately.
  • The product shall support CPUs, GPUs, embedded devices, simulators, FPGA platforms, ASIC prototypes, and custom accelerators through adapters.
  • The product shall expose capability discovery before execution.

Reporting

  • The product shall generate HTML, Markdown, JSON, CSV, and future PDF reports.
  • Reports shall include summary, configuration, device information, metrics, charts, recommendations, historical comparison, and metadata.

Visualization

  • The product shall support timeline, roofline, latency distribution, memory, scaling, comparison, and utilization charts.
  • Visualization shall consume stored benchmark data and shall not execute benchmarks directly.

CLI

  • The CLI shall include benchmark, profile, compare, report, dashboard, export, doctor, and version commands.
  • The CLI shall support human-readable and machine-readable output.
  • CLI errors shall provide causes and suggested solutions.

Plugin System

  • The product shall discover plugins using Python entry points.
  • Plugins shall register hardware, models, metrics, reports, visualizations, exporters, CLI commands, and third-party integrations.
  • Plugin descriptors shall be versioned and validated.

Configuration

  • The product shall support YAML, JSON, environment variables, and CLI options.
  • Precedence shall be CLI, environment, configuration file, defaults.
  • The product shall support profiles and inheritance.
  • Resolved configuration shall be persisted with benchmark sessions.

Export

  • The product shall export results as JSON, CSV, Markdown, HTML, and future PDF.
  • Exports shall preserve metric units, sources, and provenance.

Package Management

  • The product shall support pip install aihw-bench.
  • The product shall support uv add aihw-bench.
  • The product shall prepare metadata for Conda-Forge, Docker, Homebrew, and future package managers.

Documentation

  • The product shall provide installation, quick start, tutorials, examples, API reference, CLI reference, architecture, plugin development, FAQ, developer guide, performance guide, roadmap, and contribution guide.

Release System

  • The product shall release from semantic-version Git tags.
  • Releases shall build wheel, source distribution, checksums, SBOM, release notes, and documentation.
  • Releases shall verify installation on Windows, Linux, and macOS before publication.

7. Non-Functional Requirements

Performance

  • Benchmark overhead must remain minimal.
  • Measured regions must avoid unnecessary allocation and report generation.
  • Optional heavy profilers must be opt-in.

Scalability

  • The architecture must support single-run local benchmarks and future large benchmark campaigns.
  • Storage and visualization designs must handle large observation sets.

Reliability

  • Failed and partial sessions must remain inspectable.
  • Artifact writes should be atomic for canonical files.
  • Public releases must pass CI.

Security

  • Inputs must be validated.
  • Configuration must not execute code.
  • File handling must prevent path traversal.
  • Reports must avoid exposing secrets by default.

Usability

  • CLI commands must be discoverable.
  • Error messages must include actionable next steps.
  • Quick-start workflows must work without heavyweight optional runtimes.

Accessibility

  • Documentation must be readable without relying only on color.
  • CLI output must have machine-readable alternatives.
  • Reports should use accessible contrast and structured headings.

Maintainability

  • Clean architecture boundaries must be enforced.
  • Public APIs require documentation and versioning.
  • Modules must remain small and focused.

Extensibility

  • Plugins must support new backends, metrics, reports, visualizations, exporters, and CLI commands.
  • Core code must not need modification for third-party hardware support.

Portability

  • Core package must work on Windows, Linux, and macOS.
  • Optional backend dependencies may be platform-specific but must fail gracefully when unavailable.

8. Success Metrics

  • Installation success rate above 95% for supported platforms.
  • First benchmark completed within 10 minutes for new users.
  • Benchmark session reproducibility metadata completeness above 95%.
  • Core command startup time under 500 ms on common developer machines.
  • Test coverage at or above 90% by 1.0.0.
  • Documentation pages for 100% of public commands and public APIs.
  • PyPI downloads increasing month over month after stable release.
  • At least 10 external contributors by the first stable community phase.
  • At least 5 third-party plugins or integrations by 2.0.
  • CI release pipeline success rate above 95% after stabilization.
  • Cross-platform installation verification for every release.
  • Open issue triage median under 14 days during active maintenance.
  • Security vulnerability acknowledgement within 7 days.
  • GitHub stars, forks, and discussions used as community health indicators.

9. Product Roadmap

Prototype

  • Architecture and PRD complete.
  • Governance, constitution, release strategy, and documentation baseline.
  • Minimal package metadata and CLI diagnostics.

v0.1

  • Domain model foundation.
  • Configuration resolver.
  • CLI skeleton.
  • Filesystem session store design implementation.
  • CPU-safe reference benchmark path.

v0.2

  • Benchmark execution lifecycle.
  • Warmup, measurement, retries, failure states.
  • Core metrics.
  • JSON and Markdown reports.

v0.3

  • PyTorch backend.
  • ONNX Runtime backend.
  • Optional TensorFlow Lite backend.
  • Backend capability discovery.

v0.5

  • HTML reports.
  • Plotly visualizations.
  • Historical comparison.
  • CSV export.
  • Regression thresholds.

v1.0

  • Stable public API.
  • Stable CLI.
  • Plugin API.
  • Full docs site.
  • 90%+ coverage.
  • Automated multi-platform release pipeline.

v2.0

  • Simulator and embedded backends.
  • Local dashboard.
  • PDF reports.
  • Advanced plugin ecosystem.
  • Power and thermal metric extensions.

v5.0

  • Distributed benchmark campaigns.
  • Hardware lab orchestration.
  • Vendor-neutral result exchange format.
  • Strong ecosystem integrations.

10. Open Source Strategy

Community Model

AIHW-Bench should be open-source first. Development happens in public through issues, pull requests, discussions, documentation updates, and tagged releases.

Issue Templates

Issue templates collect reproduction steps, environment details, expected behavior, actual behavior, logs, security warnings, and compatibility impact.

Pull Requests

Pull requests must include summary, testing evidence, documentation updates, architecture impact, compatibility impact, and constitution compliance.

Discussions

Discussions should support roadmap proposals, plugin ideas, backend requests, benchmarking methodology questions, and community help.

Documentation

Documentation is part of the product, not a secondary artifact. Features are incomplete without user and developer documentation.

Contribution Workflow

  1. Open or find an issue.
  2. Discuss design for significant changes.
  3. Implement with tests and documentation.
  4. Pass CI.
  5. Receive review.
  6. Merge with changelog updates when user-facing behavior changes.

Maintainers

Maintainers own architecture quality, release integrity, review standards, security response, community behavior, and compatibility policy.

Review Process

Reviews prioritize correctness, reproducibility, security, maintainability, test coverage, documentation, and public API stability.

Community Guidelines

The code of conduct governs project spaces. The constitution governs engineering decisions.

11. Distribution Strategy

GitHub Releases

GitHub Releases provide canonical release notes, artifacts, checksums, SBOMs, and source links.

PyPI

PyPI supports standard Python installation with:

pip install aihw-bench

TestPyPI

TestPyPI validates publishing before production releases and supports release pipeline dry runs.

GitHub Pages

GitHub Pages hosts versioned public documentation at:

https://shashi065.github.io/aihw-bench/

Docker Hub and Container Registries

Docker images support reproducible benchmark environments, CI workflows, and lab automation.

Conda-Forge

Conda-Forge supports scientific, research, and university users who standardize on Conda environments.

Homebrew

Homebrew supports macOS and Linux CLI users who prefer system package manager installation.

Winget

Winget supports Windows users and enterprise-managed installations.

Chocolatey

Chocolatey supports Windows developer workflows and automation scripts.

Snap

Snap supports Linux desktop and distribution-neutral installation.

Nix

Nix supports reproducible development environments and research workflows.

Future Package Managers

The architecture keeps package metadata and release artifacts portable so future ecosystems can be added without redesigning the project.

12. Long-Term Vision

1 Year

AIHW-Bench should provide a stable foundation for local benchmarking, configuration, session persistence, core reports, CPU/PyTorch/ONNX Runtime support, and early plugin development.

3 Years

AIHW-Bench should support embedded devices, simulators, FPGA workflows, richer profiling, dashboard views, and a growing plugin ecosystem.

5 Years

AIHW-Bench should become a trusted tool for AI hardware research and engineering teams, with vendor plugins, academic adoption, reproducibility bundles, and strong benchmark comparison workflows.

10 Years

AIHW-Bench should be an industry-recognized benchmarking framework that connects ML workloads, hardware architecture, compiler stacks, embedded deployment, simulation, and accelerator validation through one open ecosystem.

Product Principles

  • Reproducibility is a feature.
  • Metrics without units and assumptions are incomplete.
  • Hardware abstraction must not erase hardware-specific truth.
  • Plugins extend the ecosystem, but core contracts define quality.
  • Documentation and release engineering are part of product quality.
  • The product should remain useful to beginners without becoming shallow for experts.