Optimizing Neural Networks for TinyML

Abstract:
Embedded devices have dedicated acceleration hardware for machine learning such as parallel instruction sets for CPUs, multi-purpose GPUs with vision kernels, Digital Signal Processors for audio processing, or Neural Processing Units specialized to run neural networks. All of this hardware requires neural network models that are optimized using techniques such as weight clustering, pruning, and most importantly quantization. We will discuss details of such optimizations for different use cases, different hardware, their impact on performance, and the accuracy trade-off.

Program:
17:30 Welcome chat
18:00 Talk
18:45 Discussion
19:00 Networking (Impact Hub)

Machine Learning Meetups (MLMU) is an independent platform for people interested in Machine Learning, Information Retrieval, Natural Language Processing, Computer Vision, Pattern Recognition, Data Journalism, Artificial Intelligence, Agent Systems and all the related topics. MLMU is a regular community meeting usually consisting of a talk, a discussion and subsequent networking. Except of Prague, MLMU also spread to Brno, Bratislava and Košice.
http://www.mlmu.cz/