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mcdnn

Provide GPU-accelerated primitives for deep neural network operations.

Updated Aug 27, 2026
Search Tags:#performance#deep learning#framework#convolution#neural networks#gpu acceleration
One-click install
npx skills add https://github.com/dongg622/china-ai-chip-skill --skill mcdnn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mcdnn
Source: https://github.com/dongg622/china-ai-chip-skill/tree/main/MetaX/mcdnn
Command: npx skills add https://github.com/dongg622/china-ai-chip-skill --skill mcdnn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill facilitates high-performance GPU-based deep neural network computations, streamlining both training and inference workflows.

Core Features & Use Cases

  • Core Operator Optimization: Provides highly optimized implementations of convolutions, pooling, activations, normalization, and softmax for deep learning models.
  • Application Scope: Suitable for CNN training, inference deployment, and deep learning framework acceleration across various AI tasks.
  • Use Case: Enable rapid prototyping of neural networks by utilizing mcDNN's core API for convolutional layers and activation functions with minimal setup.

Quick Start

Use the mcDNN skill to perform a 2D convolution forward pass with specified tensor dimensions.

Frequently Asked Questions about mcdnn

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I accelerate deep learning model training on GPU architectures?▼

This Skill provides GPU-accelerated primitives for convolutions, pooling, and activation functions to streamline deep learning training and inference workflows on hardware like Ascend.

What are GPU-accelerated primitives for convolutional neural networks?▼

GPU-accelerated primitives are highly optimized implementations of convolutions, pooling, activations, normalization, and softmax designed to execute high-performance deep neural network computations on supported hardware like Ascend.

Can I use this to run 2D convolution forward passes with custom tensor dimensions?▼

Yes, you can use the core API to perform a 2D convolution forward pass by specifying your custom tensor dimensions, enabling rapid prototyping of neural networks with minimal setup.

Do I need specific deep learning frameworks or libraries to integrate GPU acceleration?▼

Yes, integrating these GPU acceleration primitives requires mcdnn and its associated libraries to embed the optimized operators into your existing deep learning framework for training and deployment.

What is the best way to optimize CNN inference deployment on Ascend hardware?▼

The best way to optimize CNN inference deployment on Ascend hardware is to apply the highly optimized core operators for convolutions, pooling, and activations provided by this GPU acceleration framework.

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