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cover of episode Efficient Deployment of Models at the Edge // Krishna Sridhar // #284

Efficient Deployment of Models at the Edge // Krishna Sridhar // #284

2025/1/17
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Krishna Sridhar: 我在威斯康星大学攻读博士学位时,研究的是数值优化,最初是为了帮助石油公司优化税务。然而,我逐渐意识到,利用机器学习解决实际问题比帮助石油公司避税更有意义。因此,我转入了人工智能领域,并参与了苹果Core ML的设计,这是一个在边缘设备上部署模型的推理引擎。边缘计算的吸引力在于,它既能提供快速的用户体验,又能保护用户隐私,因为数据无需离开设备。例如,面容ID需要在300毫秒内完成身份验证,这需要在本地设备上运行复杂的神经网络。现在,我致力于构建能够应对快速变化的人工智能和硬件生态系统的稳定基础设施,让创新者能够轻松地将他们的AI模型应用到各种设备上,无论是最新的还是旧款的硬件。

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Krishna Sridhar) is an experienced engineering leader passionate about building wonderful products powered by machine learning.

Efficient Deployment of Models at the Edge // MLOps Podcast #283 with Krishna Sridhar, Vice President of Qualcomm.

Big shout out to Qualcomm) for sponsoring this episode!

// Abstract Qualcomm® AI Hub helps to optimize, validate, and deploy machine learning models on-device for vision, audio, and speech use cases.

With Qualcomm® AI Hub, you can:

Convert trained models from frameworks like PyTorch and ONNX for optimized on-device performance on Qualcomm® devices. Profile models on-device to obtain detailed metrics including runtime, load time, and compute unit utilization. Verify numerical correctness by performing on-device inference. Easily deploy models using Qualcomm® AI Engine Direct, TensorFlow Lite, or ONNX Runtime.

The Qualcomm® AI Hub Models repository contains a collection of example models that use Qualcomm® AI Hub to optimize, validate, and deploy models on Qualcomm® devices.

Qualcomm® AI Hub automatically handles model translation from source framework to device runtime, applying hardware-aware optimizations, and performs physical performance/numerical validation. The system automatically provisions devices in the cloud for on-device profiling and inference. The following image shows the steps taken to analyze a model using Qualcomm® AI Hub.

// Bio Krishna Sridhar leads engineering for Qualcomm™ AI Hub, a system used by more than 10,000 AI developers spanning 1,000 companies to run more than 100,000 models on Qualcomm platforms.

Prior to joining Qualcomm, he was Co-founder and CEO of Tetra AI which made its easy to efficiently deploy ML models on mobile/edge hardware.

Prior to Tetra AI, Krishna helped design Apple's CoreML which was a software system mission critical to running several experiences at Apple including Camera, Photos, Siri, FaceTime, Watch, and many more across all major Apple device operating systems and all hardware and IP blocks.

He has a Ph.D. in computer science from the University of Wisconsin-Madison, and a bachelor’s degree in computer science from Birla Institute of Technology and Science, Pilani, India.

// MLOps Swag/Merch https://shop.mlops.community/)

// Related Links Website: https://www.linkedin.com/in/srikris/)

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/) Connect with Krishna on LinkedIn: https://www.linkedin.com/in/srikris/)