accelforge 1.0.365

AccelForge

Model, design, and explore tensor algebra accelerators.

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AccelForge is a framework for modeling, designing, and exploring tensor algebra accelerators. It uses HWComponents as a backend for area, energy, latency, and leak power estimates.

Learn more at the website or on GitHub.

⚡ Features

• Flexible Full-Stack Modeling of a wide variety of devices, circuits, architectures, workloads, and mappings. We integrate with HWComponents, with easily-modifiable models for component area, energy, latency, and leak power.
• Fast and optimal mapping of workloads onto architectures, yielding the best-possible performance and energy efficiency.
• Fusion-aware mapping that optimizes fusion for cascades of Einsums, enabling end-to-end optimization of entire workloads.
• Heterogenous Architectures that can include multiple types of compute units.
• Strong input validation via Pydantic, with clear error reports for invalid specifications.
• Pythonic Interfaces that enable easy automation and integration with other tools.

📦 Install

pip install accelforge

🧪 Examples

See examples/ for architectures and workloads, and notebooks/ for tutorials.

📚 Cite

If you use AccelForge in your work, please see Citing AccelForge for the relevant papers.