The Energy-Efficient Multimedia Systems (EEMS) group at MIT has a Tutorial on Hardware Architectures for Deep Neural Networks. Here is the website: http://eyeriss.mit.edu/tutorial.html
All Slides in one PDF.
The slides explain a lot of convolutional Nerual Networks and how they work. It also describes many different topics ranging from the architectures for the winners of ImageNet and how their success also correlate with the use of GPUs for processing. The later part is more on the details in computation, what is computed where and how.
If you wish to check out each individual topic, they are also splitted into several different slides:
- Background of Deep Neural Networks [ slides ]
- Survey of DNN Development Resources [ slides ]
- Survey of DNN Hardware [ slides ]
- DNN Accelerator Architectures [ slides ]
- Advanced Technology Opportunities [ slides ]
- Network and Hardware Co-Design [ slides ]
- Benchmarking Metrics [ slides ]
- Tutorial Summary [ slides ]
- References [ slides ]
This free course by Udacity and NVIDIA teaches you how to get your mind around and do parallell programming with the GPU. You will use the CUDA programming environment (that you also use in deep learning) in order to use the GPU for your processing. You will have access to high-end GPU machines. Things you will learn centers around image processing.
It is an awesome age we live in where the knowledge you need for tomorrow is available for free for everyone (with a computer, and an internet connection). There is more to learn than there is time to learn it in. We all can become experts in our fields. You must, however find places and situations to put your knowledge into practice so that it will not wane away. I think it is awesome that Nvidia has a learning institute with free courses to help you learn cutting edge stuff. By learning from a company focused on the advancing of the field, and who actually has only to gain from us learning us more, will keep you on the frontiers of the field.