# Internal Harvard TinyML course talk recordings

**URL:** <https://discuss.tinyml.seas.harvard.edu/t/internal-harvard-tinyml-course-talk-recordings/427>\
**Category:** Education\
**Created:** [March 5, 2021, 3:41am UTC](https://discuss.tinyml.seas.harvard.edu/t/internal-harvard-tinyml-course-talk-recordings/427 "2021-03-05T03:41:27Z")\
**Posts on this page:** 1\
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**Author:** ![rghosal](https://avatars.discourse-cdn.com/v4/letter/r/bbce88/32.png) [@rghosal](https://discuss.tinyml.seas.harvard.edu/u/rghosal)\
**Post date:** [March 5, 2021, 3:41am UTC](https://discuss.tinyml.seas.harvard.edu/t/internal-harvard-tinyml-course-talk-recordings/427/1 "2021-03-05T03:41:28Z")

</div>

Hello TinyML enthusiasts! We recently released all the industry talks from the internal Harvard offering of the Intro to TinyML course publicly. You can find the entire playlist [here](https://www.youtube.com/playlist?list=PLJ-4lGVfhv34qW0r4dt_qXyY3-HezeGUo).

Hope you find them valuable!

### Links to individual videos along with short descriptions.

[“Recent Progress on TinyML Technologies and Opportunities” by Evgeni Gousev (Qualcomm)](https://youtu.be/AWxJY_hS_Ac): Overview of the TinyML ecosystem + roadmap and Qualcomm’s efforts in the space.

[“Common Voice” by Joshua Meyer & Jane Polak Scowcroft (Mozilla)](https://youtu.be/BTg-PUNi9jw): Voice data is essential for training tiny edge devices to do voice recognition. Unfortunately, there is a lack of such massive voice datasets in the public domain. This talk outlines Mozilla’s efforts in creating Common Voice, a crowd-sourced, multilingual speech corpus.

[“Data Collection Design for Real World TinyML” by Sacha Krstulović (Audio Analytic)](https://youtu.be/ce5y5myvLug): Sound recognition is a key part of inferring context and environmental features in edge devices; how to collect reliable data for differentiating between sounds?

[“Hotword Detection” by Alex Gruenstein (Google)](https://youtu.be/FsJUU4rPrX8): Algorithms for doing hotword detection (“Ok Google”!)

[“EdgeML: Algorithms for TinyML” by Prateek Jain (Microsoft)"](https://youtu.be/TfVHmYT6Rj4): Algorithms and tools for tiny models for ML inference on the edge.

[“TVM: An End to End Deep Learning Compiler Stack” by Thiery Moreau (OctoML)](https://youtu.be/JmWWAnHBDx0): Compiler infrastructures for seamlessly deploying deep neural networks across a wide range of hardware platforms.

[“Artificial Neural Networks and Tools for Microcontrollers” by Danilo Pau (STMicroelectronics)](https://youtu.be/enUuGgPX_HY): Challenges in interoperability across DNN frameworks and microcontroller targets + benchmarking tools.

[“TensorFlow Lite Micro” by Pete Warden (Google)](https://youtu.be/wjiPn7gm-vA): The journey behind building TensorFlow Lite Micro.

[“MLOps for TinyML” by Daniel Situnayake (Edge Impulse)](https://youtu.be/CntBTQLCaFc): DevOps is a software development methodology for streamlining building, testing, and releasing software. This talk covers tools + best practices for doing so for complex ML pipelines.

[“CMSIS-NN and Library Optimizations” by Felix Thomasmathibalan (ARM)](https://youtu.be/S_qH2yQlgpw): Optimizations for neural network kernels targeting ARM microcontrollers for edge devices.

[“Endpoint AI and the Advent of the microNPU” by Tomas Edsö (ARM)](https://youtu.be/0ac8Mdc3t68): Creating a configurable Neural Processing Unit for ML inference on edge devices.

[“Efficient Dot Products (Or, Scaling ML Workloads)” by Erich Plondke (Qualcomm)](https://youtu.be/s1RogqIS5ZI): Efficient matrix operations for low power Digital Signal Processors (DSPs), now ubiquitous in mobile phones.

[“TFLite Micro Benchmarks” by Nat Jeffries (Google)](https://youtu.be/Ui9dGdhuK_M): Standardizing benchmarking for TinyML hardware targets.

[“Privacy in Context” by Susan Kennedy (Harvard)](https://youtu.be/1lAsEmxCbt8): Identifying and mitigating challenges to privacy in TinyML applications.

[“Responsible AI with TensorFlow” by Tulsee Doshi (Google)](https://youtu.be/2CPouyjQkXA): Identifying and mitigating fairness concerns in ML algorithms and models.