Explore a live demonstration of the world's fastest inference engine for Arm Cortex-M microcontrollers. Discover how Plumerai's engine outperforms TensorFlow Lite for Microcontrollers with CMSIS-NN kernels, achieving 40% lower latency and 49% less RAM usage without compromising accuracy. Learn about the techniques used to achieve these speedups and memory improvements, including optimized memory planning and INT8 code generation. Examine benchmarks for popular neural network models and gain insights into the MLPerf Tiny models. Understand the machine learning flow and the tasks of an inference engine, with a focus on the TFLM example. Get introduced to Plumerai's public benchmarking service, allowing you to test your own models with their cutting-edge inference engine.
TinyML Talks - Demoing the World’s Fastest Inference Engine for Arm Cortex-M
Overview
Syllabus
Intro
tinyML Summit 2022 Miniature dreams can come true. March 28-30, 2022 Hyatt Regency San Francisco Airport
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Or from: the world's fastest Cortex-M inferen
How did we get here?
The machine learning flow
The tasks of an inference engine
An inference engine example: TELM
A closer look at the results
More off-the-shelf models
A closer look at the MLPerf Tiny models
How to beat the competition?
Memory planning: a (rotated) game of T
Memory planning for an example model
A much better memory plan
Even better: lower granularity planning
Memory planning at Plumerai: summary
Optimized INT8 code for speed
Model-specific code generation
The world's fastest Cortex-M inference
What can Plumerai mean for you?
Public benchmarking service: try it yours
Arm: The Software and Hardware Foundation for tin
EDGE IMPULSE The leading edge ML pla
Enabling the next generation of Sensor and Hearable pro to process rich data with energy efficiency
maxim integrated Maxim Integrated: Enabling Edge Intelligence
BROAD AND SCALABLE EDGE COMPUTING PORTFOLIO
SYNTIANT
Taught by
tinyML
