Mace

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MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms. It supports deployment on Android, iOS, and Linux, focusing on performance and power efficiency.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is mace?

MACE (Mobile AI Compute Engine) is an open-source deep learning inference framework specifically designed for mobile heterogeneous computing platforms. It optimizes AI model deployment on Android, iOS, Linux, and Windows devices by focusing on performance, power consumption, responsiveness, and memory usage. Key optimizations include NEON, OpenCL, and Hexagon acceleration, Winograd algorithm for convolution, and chip-dependent power options. MACE also prioritizes model protection through techniques like converting models to C++ code and literal obfuscations. It supports popular model formats such as TensorFlow, Caffe, and ONNX, making it a versatile tool for developers working with mobile AI applications.

Best used for

Ideal for developers and data scientists who need to deploy deep learning models efficiently on mobile devices, optimize for performance and power consumption, and ensure model protection. Especially valuable for those working with Android, iOS, Linux, and Windows platforms requiring heterogeneous computing.

Common actions

optimize deep learning

deploy AI models

accelerate inference

protect models

"AI Agents"github copilotopen-sourceface swappingdeepfakeworkflowsautomated workflowlow-code/no-codecollaboration

Capabilities

Key features

NEON/OpenCL/Hexagon optimization
Winograd algorithm
Chip-dependent power options
Graph-level memory optimization
Model protection
TensorFlow/Caffe/ONNX support
Cross-platform deployment

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of deep learning models does MACE support?

MACE supports a variety of popular deep learning model formats, including TensorFlow, Caffe, and ONNX. This broad compatibility allows developers to integrate models from different frameworks into their mobile applications using MACE.

How does MACE optimize performance on mobile devices?

MACE optimizes performance through several techniques, including utilizing NEON, OpenCL, and Hexagon for acceleration. It also incorporates the Winograd algorithm to speed up convolution operations and offers chip-dependent power options for efficient resource management.

Can MACE help protect my AI models?

Yes, model protection is a high priority for MACE. It introduces various techniques such as converting models to C++ code and literal obfuscations to help secure your deep learning models when deployed on mobile platforms.

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AI Agents & Automation › AI Frameworks & Infra

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