Exploring the Open AI Lab's Cutting-Edge AI Software Platform

Exploring the Open AI Lab's Cutting-Edge AI Software Platform

Table of Contents:

1. Introduction
2. Open Air Lab Overview
3. AI Software Platform
4. Hardware Platform
5. Open AI Software Platform Layers
   • Applications Layer
   • Framework Layer
   • Dummy Library Layer
6. Open Air Lab's Work
7. Optimized Cafe, TensorFlow, and AmaxNet
   • Performance Comparison
   • Dynamic Scheduler
   • Extended Operators
8. Tianjin Framework
   • Inference Engine
   • Compatibility with Cafe and TensorFlow
   • Performance Comparison
9. Domain Library
   • Face Detection
   • Face Recognition
   • Object Detection
   • Speech Recognition
10. Tools and Support
    • Debugging and Profiling Tools
    • Server Tools
    • Local Framework Tools
11. Conclusion

Introduction

👋 Welcome! In this article, we will explore the open-air lab and AI software platform developed by Harlan Robotics and Sarita. We'll delve into the hardware and software components, including the optimized Cafe, TensorFlow, and AmaxNet frameworks. Additionally, we'll take a closer look at the Tianjin framework and its compatibility with existing frameworks. Furthermore, we'll discuss the domain libraries available, such as face detection, face recognition, object detection, and speech recognition. Let's dive right in!

Open Air Lab Overview

The open-air lab, initiated by Arm, Harlan Robotics, and Sarita in December 2016, focuses on creating an AI software platform for edge computing. With a particular emphasis on embodied devices, this lab aims to develop an AI software platform to enable advanced computing capabilities. Headquartered in Shanghai, with a division in Beijing, this open-air lab is dedicated to exploring artificial intelligence solutions for various industries.

AI Software Platform

The AI software platform developed by the open-air lab serves as the backbone of their research and development efforts. This software platform is designed to support AI computing across different devices, including edge gateways and embodied devices. Comprising three layers, the platform provides a robust foundation for developing AI applications and leveraging machine learning frameworks. Let's explore each layer in detail.

Hardware Platform

At the heart of the AI software platform lies the hardware platform. To enable efficient AI computing, the platform combines CPU-class GPU, deep learning accelerators, and DSP (Digital Signal Processor). This heterogeneous computing platform provides the necessary computational power for running AI algorithms on various hardware configurations. The open-air lab supports the use of ARM's SOC (System on Chip) architecture, which encompasses different CPU and GPU configurations.

Open AI Software Platform Layers

The open AI software platform is divided into three layers: applications, framework, and dummy library.

Applications Layer

The applications layer provides a user-friendly interface for accessing AI functionalities without requiring in-depth knowledge of the underlying frameworks or libraries. Users can interact with the software platform by using simple APIs provided by the open-air lab. These APIs abstract the complexities of deep learning frameworks, allowing developers to focus on building AI applications specific to their use cases. Supported applications include face detection, face recognition, object detection, and speech recognition.

Framework Layer

The framework layer of the open AI software platform includes widely-used deep learning frameworks such as Cafe, Cafe HRT, TensorFlow, and Tianjin. These frameworks provide the necessary tools and algorithms to train and execute machine learning models. The open-air lab ensures compatibility of their platform with these frameworks to leverage existing models and facilitate seamless integration for developers.

Dummy Library Layer

The dummy library layer acts as an intermediary between the applications layer and the framework layer. It includes a collection of application-specific libraries, enabling developers to access specific AI functionalities without having to know the details of the underlying framework. These libraries encompass algorithms related to computer vision, speech recognition, and other sensor-related domains. The dummy library adapts to the different frameworks supported by the platform, ensuring maximum flexibility for developers.

Open Air Lab's Work

The open-air lab has made significant contributions to the field of AI computing. Their dedicated team focuses on optimizing existing frameworks and developing new tools to enhance performance and usability. Let's take a closer look at some of their work:

1. Optimized Cafe, TensorFlow, and AmaxNet

   The open-air lab has invested considerable effort in optimizing well-known deep learning frameworks such as Cafe, TensorFlow, and AmaxNet. By leveraging dynamic Scheduling techniques, they have improved the performance of these frameworks on diverse hardware platforms. The dynamic scheduler intelligently assigns operators to run on CPU, GPU, or DSP, based on the specific network requirements. This optimization results in improved throughput and more efficient utilization of computing resources.

   Additionally, the open-air lab has extended the capabilities of these frameworks by supporting additional operators and libraries. Through careful selection and optimization, they ensure that different networks use the most suitable libraries, maximizing performance for each network's unique set of operators.

2. Tianjin Framework

   As part of their research, the open-air lab has developed the Tianjin framework, specifically designed for inference tasks. The Tianjin framework supports CPUs, GPUs, DSPs, and facilitates dynamic scheduling of operators across heterogeneous resources. The framework's flexibility allows developers to utilize existing Cafe and TensorFlow models without modifying their software stack. This compatibility enables developers to seamlessly transition from traditional frameworks to Tianjin, providing improved performance without sacrificing familiarity.

3. Domain Library

   The open-air lab has also developed a domain-specific library to cater to various use cases. Their domain library includes algorithms and models for tasks such as face detection, face recognition, object detection, and speech recognition. These pre-trained models are readily available, enabling developers to leverage them for their own applications. The lab continues to expand this library, focusing on domains related to sensors for future releases.

Optimized Cafe, TensorFlow, and AmaxNet

🔬 In this section, we'll dive deeper into the optimization efforts of the open-air lab for Cafe, TensorFlow, and AmaxNet frameworks. We'll explore the performance improvements, dynamic scheduling techniques, and extended operator support. Let's get started!

Performance Comparison

📊 The open-air lab has dedicated substantial resources to improve the performance of existing deep learning frameworks. By leveraging their expertise, they have achieved significant performance enhancements. Let's compare the performance of optimized Cafe, TensorFlow, and AmaxNet frameworks with their vanilla versions.

On an ARM Cortex A53 processor, the performance gains are remarkable. Single-core performance on optimized Cafe exceeds the baseline Cafe by 5 times. Similarly, optimized TensorFlow and AmaxNet showcase impressive performance improvements. This enhanced performance extends across various platforms, including ARM Cortex-A72, Google Night, and Mobile Night. These optimization efforts ensure that AI computing tasks can be executed efficiently on a range of devices.

⭐ Pros:

• Significant performance improvements achieved for Cafe, TensorFlow, and AmaxNet frameworks.
• Enhanced single-core performance enables efficient execution.

⚠️ Cons:

• Limited details regarding the specific techniques employed for optimization.

Dynamic Scheduler

🔄 The open-air lab has introduced dynamic scheduling techniques to optimize AI computing. By integrating a dynamic scheduler into Cafe, they have enabled more efficient utilization of CPU and GPU resources. This dynamic scheduling allows different operators within a network to run on the most suitable hardware, resulting in improved throughput and reduced latency.

The dynamic scheduler also offers flexibility in choosing the libraries associated with each operator. Not all libraries perform equally well for every operator or network. By allowing the dynamic scheduler to select the most suitable library, the open-air lab ensures that operators can leverage the best-performing libraries available.

⭐ Pros:

• Improved resource utilization through intelligent allocation of operators to CPU or GPU.
• Flexibility in library selection for optimal performance.

⚠️ Cons:

• Limited information on the specific mechanisms and algorithms employed in dynamic scheduling.

Extended Operators

🔌 To further enhance the compatibility and performance of their frameworks, the open-air lab has developed extended operators. These operators address scenarios where the frameworks' default operators do not provide optimal results. The extended operators can leverage specific hardware, such as DSP or CPU, to execute certain operations more efficiently.

By supporting extended operators, the open-air lab enables developers to utilize the optimal hardware resources for specific tasks. This flexibility ensures that the AI software platform can cater to a wide range of network architectures and models, maximizing performance across different use cases.

⭐ Pros:

• Enhanced performance through extended operator support.
• Flexible utilization of hardware resources for improved efficiency.

⚠️ Cons:

• Limited information on the specific operators supported and the hardware accelerated.

Tianjin Framework

💡 In this section, we'll explore the Tianjin framework developed by the open-air lab. This framework provides powerful capabilities for AI inference tasks. We'll discuss the inference engine, its compatibility with traditional frameworks, and the performance benefits it offers. Let's dive in!

Inference Engine

The Tianjin framework focuses on enabling efficient and high-performance inference for AI applications. It is designed to leverage CPUs, GPUs, and DSPs efficiently, utilizing dynamic scheduling techniques to optimize resource allocation.

The framework provides a seamless transition for developers who are already using frameworks like Cafe and TensorFlow. Through compatible APIs and libraries, developers can link their existing software stack with the Tianjin framework. This compatibility ensures that models trained with Cafe or TensorFlow can be executed on the Tianjin framework without the need for significant modifications.

Compatibility with Cafe and TensorFlow

🤝 The Tianjin framework emphasizes compatibility with Cafe and TensorFlow, two widely-used deep learning frameworks. The open-air lab has ensured that the APIs and libraries provided by Tianjin are compatible with the corresponding functionalities of Cafe and TensorFlow. By linking the appropriate libraries, developers can seamlessly transition from their existing Cafe or TensorFlow workflow to the Tianjin framework.

This compatibility and ease of integration allow developers to harness the performance benefits of the Tianjin framework without the need for extensive retraining or learning new frameworks. It enables an efficient and hassle-free adoption process for AI inference tasks.

Performance Comparison

🏋️ The open-air lab has conducted performance comparisons between the Tianjin framework and other deep learning frameworks. These comparisons showcase the superior performance achieved by Tianjin, especially when running on ARM Cortex A53 processors.

On the ARM Cortex A53, Tianjin outperforms both Cafe and optimized Cafe by a significant margin. In benchmark tests using the SqueezeNet and MobileNet architectures, Tianjin achieves up to 22.46 times the performance of the baseline Cafe framework. These results demonstrate the impressive efficiency and speed of the Tianjin framework.

⭐ Pros:

• Efficient interference engine for AI applications.
• Seamless compatibility with Cafe and TensorFlow frameworks.
• Superior performance on ARM Cortex A53 processors.

⚠️ Cons:

• Limited details on specific benchmark tests and additional hardware configurations.

Domain Library

🔍 In this section, we'll explore the domain-specific library developed by the open-air lab. This library provides a range of algorithms and models for various AI domains. We'll discuss the different domains covered, including face detection, face recognition, object detection, and speech recognition. Let's delve into the details!

Face Detection

👤 Face detection is a fundamental task in computer vision, and the open-air lab offers an algorithm based on MTC (Multi-task Cascaded Convolutional) networks for this purpose. The MTC-based face detection algorithm provides accurate and efficient face detection capabilities. It enables AI applications to recognize and locate faces within visual data, unleashing the potential for advanced facial analysis and identification.

Face Recognition

🔍 Building upon the face detection capabilities, the open-air lab introduces face recognition algorithms. By utilizing MTC networks, these algorithms can recognize and verify individuals from visual data. The face recognition algorithms enable applications to perform facial authentication, access control, and personalization features. With the open-air lab's face recognition algorithms, developers can empower their AI applications with the ability to identify individuals accurately.

Object Detection

🔎 The open-air lab's domain library covers object detection algorithms as well. Object detection algorithms enable AI applications to identify and locate specific objects within visual content. Their algorithms are designed to support a wide range of objects, ensuring flexibility and versatility in object detection tasks. By leveraging the object detection algorithms from the open-air lab's domain library, developers can equip their AI applications with enhanced object recognition capabilities.

Speech Recognition

🎙️ Speech recognition, an essential domain for AI applications, is also supported by the open-air lab's domain library. They provide algorithms for acoustic modeling, language modeling, and other components required for accurate speech recognition. These algorithms can be leveraged to enable AI applications with voice command support and voice-driven interactions. With the open-air lab's speech recognition algorithms, developers can introduce hands-free and voice-enabled capabilities into their applications.

⭐ Pros:

• Domain-specific algorithms for face detection, face recognition, object detection, and speech recognition.
• Application-ready models for efficient integration.
• Supports a wide range of AI use cases across different domains.

⚠️ Cons:

• Limited information on additional AI domains covered by the domain library.

Tools and Support

🔧 In this section, we'll highlight the tools and support provided by the open-air lab to facilitate AI application development. These tools encompass debugging and profiling tools, server tools, and local framework tools. Let's explore these resources in detail!

Debugging and Profiling Tools

🐞 To support developers in their AI application development journey, the open-air lab provides debugging and profiling tools. These tools help developers identify and rectify issues within their AI models and software stack. The debugging tools offer insights into model behavior, allowing developers to analyze and debug their AI algorithms effectively. The profiling tools help developers understand the performance characteristics of their AI applications, enabling optimization for enhanced efficiency.

Server Tools

🖥️ The open-air lab acknowledges the importance of server-Based ai computing. To this end, they provide server tools designed for ARM and x86 architectures. These tools facilitate efficient AI model deployment and management on server environments. Developers can leverage the server tools to optimize their AI applications for server-based deployments, enabling scalable and high-performance AI computing.

Local Framework Tools

📲 For developers focused on edge computing and deploying AI applications on embedded devices, the open-air lab offers tools specifically tailored to local frameworks. These tools enable developers to optimize their models and inference performance on resource-constrained devices. By providing specialized tools for local frameworks, the open-air lab ensures that developers can harness the full potential of their AI applications, even in edge computing scenarios.

⭐ Pros:

• Debugging and profiling tools to aid AI application development.
• Server tools for efficient deployment and management of AI models on server environments.
• Local framework tools for optimizing AI applications in resource-constrained edge computing scenarios.

⚠️ Cons:

• Limited information on specific debugging, profiling, server, and local framework tools provided.

Conclusion

🔚 In this article, we've explored the open-air lab and its AI software platform, developed by Harlan Robotics and Sarita. We've discussed the hardware and software aspects, including the optimized Cafe, TensorFlow, and AmaxNet frameworks. Additionally, we've delved into the Tianjin framework and its compatibility with existing frameworks. Furthermore, we explored the domain-specific library that covers face detection, face recognition, object detection, and speech recognition algorithms. Lastly, we highlighted the tools and support provided by the open-air lab to facilitate AI application development. The open-air lab's contributions hold tremendous potential for advancing AI computing across various industries.

Highlights:

• The open-air lab specializes in AI software platform development for edge computing.
• The AI software platform comprises hardware and software layers, providing a comprehensive solution.
• Optimized Cafe, TensorFlow, and AmaxNet frameworks offer improved performance and extended capabilities.
• The Tianjin framework facilitates efficient AI inference, compatible with Cafe and TensorFlow.
• The open-air lab's domain library covers face detection, face recognition, object detection, and speech recognition algorithms.
• A range of tools and support are available, including debugging and profiling tools, server tools, and local framework tools.

FAQs:

Q: Can I use the open-air lab's AI software platform on different hardware configurations?

A: Yes, the open-air lab's AI software platform is designed to be compatible with various hardware configurations. It supports SOC architectures combining CPU-class GPU, deep learning accelerators, and DSP, enabling efficient AI computing across devices.

Q: Which frameworks are compatible with the Tianjin framework?

A: The Tianjin framework emphasizes compatibility with Cafe and TensorFlow, two widely-used deep learning frameworks. Developers can link their existing Cafe or TensorFlow models and libraries to the Tianjin framework without extensive modifications.

Q: Does the open-air lab provide pre-trained models for the domain library?

A: Yes, the open-air lab provides pre-trained models for face detection, face recognition, object detection, and speech recognition. These models are readily available and can be integrated into AI applications for quick deployment.

Q: Can I use the open-air lab's AI software platform for speech recognition on ARM Cortex-A53 processors?

A: Yes, the open-air lab's domain library includes speech recognition algorithms that can run on ARM Cortex-A53 processors. These algorithms enable voice command recognition and support voice-driven interactions.

Q: Does the open-air lab provide support for server-based AI computing?

A: Yes, the open-air lab offers server tools designed for ARM and x86 architectures. These tools facilitate efficient AI model deployment and management on server environments, ensuring scalable and high-performance AI computing.

Resources:

• Open Air Lab Github Repository