LVC21-113: TensorFlow Lite Delegates on Arm-based Devices

Session Abstract

TensorFlow Lite is a popular open-source framework that enables Machine Learning on mobile, embedded, and IoT devices by default using the Arm NEON instruction set. The delegate mechanism takes TensorFlow Lite one step further and provides a mechanism to use on-device hardware accelerators such as the GPU, NPU, or Digital Signal Processor.

Session Speakers

Pavel Macenauer

NXP (Senior Software Engineer)

Pavel currently develops accelerated ML backends running on GPU/NPUs and enables NXP's eIQ Machine Learning platform. He actively contributes to Linaro's Arm NN framework and as such he was one of the developers contributing to the Python enablement in its latest release. His past experiences involve the development of safety-critical RTOS/display systems for Honeywell Aerospace or image processing applications for photographers.

TensorFlow Lite is a popular open-source framework that enables Machine Learning on mobile, embedded, and IoT devices by default using the Arm NEON instruction set. The delegate mechanism takes TensorFlow Lite one step further and provides a mechanism to use on-device hardware accelerators such as the GPU, NPU, or Digital Signal Processor.

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