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Mapping of NNs to Hardware using TensorFlow Lite
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Classroom Contents
Energy-efficient On-device Processing for Next-generation Endpoint ML - tinyML Summit 2020
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- 1 Intro
- 2 Best-in-class Solution Optimized for Endpoint Al
- 3 Unified Software Development: Fastest Path to Endpoint Al
- 4 Cortex-M55:The most Al-capable Cortex-M CPU
- 5 Simplified Software Development Based on a Unified Programmer's View
- 6 Cortex-M55 and CMSIS-NN performance results
- 7 Ethos-U55 overview
- 8 Typical Ethos-U55 data flow
- 9 Ethos-U55 interfaces
- 10 Mapping of NNs to Hardware using TensorFlow Lite
- 11 An example smart speaker pipeline
- 12 Throughput-smart speaker use case
- 13 Example: Typical ML Workload for a Voice Assistant
