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Awesome GitHub RepositoriesHardware-Specific Graph Transformations

Transforms a model graph into operators optimized for specific hardware targets like GPUs or NPUs.

Distinct from Compilation Target Specifications: Focuses on transforming graph operators for hardware efficiency rather than general compilation target specifications.

Explore 2 awesome GitHub repositories matching programming languages & runtimes · Hardware-Specific Graph Transformations. Refine with filters or upvote what's useful.

Awesome Hardware-Specific Graph Transformations GitHub Repositories

AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
  • chipsalliance/chiselchipsalliance का अवतार

    chipsalliance/chisel

    4,691GitHub पर देखें↗

    Chisel is a hardware construction language and description tool used to define digital circuits. It functions as a generator that converts high-level hardware descriptions into synthesizable Verilog code for use in ASIC and FPGA design. The project enables the creation of parameterizable hardware templates and reusable digital components. It leverages functional and object-oriented programming patterns to transform complex circuit representations into finalized hardware descriptions. The toolset covers the register-transfer level design workflow, allowing users to model digital circuits usin

    Applies automated transformations and technology-specific specializations to a design before final code generation.

    Scalachip-generatorchiselchisel3
    GitHub पर देखें↗4,691
  • metavoiceio/metavoice-srcmetavoiceio का अवतार

    metavoiceio/metavoice-src

    4,202GitHub पर देखें↗

    This project is an expressive text-to-speech foundation model and voice cloning system designed to synthesize human-like speech with emotional nuance and high fidelity. It functions as a finetunable speech model that can generate audio mimicking a specific person using a reference voice sample. The system distinguishes itself through a high-performance inference engine that utilizes memory caching and hardware compilation to reduce latency during the audio generation process. It further allows for synthesis quality improvements by training the language model on custom datasets consisting of a

    Transforms the neural network graph into operators optimized for specific chipsets to minimize latency.

    Pythonaideep-learningpytorch
    GitHub पर देखें↗4,202
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