"machine learning in computer vision" VS "machine learning in computer architecture"
Hello I am a first year PHD student in the field of machine learning. I am choosing the topic between "machine learning in computer vision" and "machine learning in computer architecture". I am wondering if computer vision has more application and it can lead to a job easier than the field of computer architecture after graduation in 3 or 4 years? It seems that computer architecture is essentially ASIC/FPGA design and it belongs to hardware category, and it seems there are not much hardware position in the market compared to computer vision, is it correct? And also is it true that hardware engineer/researcher (even in NVIDIA and APPLE) are not compensated at the same level as the SWE or MLE with the same years of work/research experience? If anyone happen to know about these two fields, can you please shed some light on this? Because choosing a topic is a big commitment for a PHD, it is very hard to change the topic again after a few years in. Thanks
CV just means you’re applying the ML model to primarily images, signals, (stuff the computer can “see”) and architecture is pretty much everything else. The foundation is all the same. When you look for jobs they’re going to ask you the basics, perceptrons, linear SVM, other regression methods, backprop, then they’ll have you leetcode, and once you pass you get put on a team. If you want to work for Nvidia you need to be great at c++ and understand kernels etc. if you want to work for literally any other tech company in ML, you need to know python and all the popular libraries i.e tensorflow, scikit, torch, etc
If you are starting your PhD , don’t go just by the hot field. You will be in the program 4-5 years and the world will change a lot by then. I know people who did their PhD in VLSI because it was hot but they aren’t making as much money as the CV guys. Guess what CV was looked down upon at the time and people had trouble finding jobs. So, don’t try to plan is to the finest details and try to do really quality research with a rockstar prof.
Agree the system always overshoots before it stabilizes
Hello bannon, I wondering is it that VLSI was just as hot a few years ago like CV and ML today?
CV is a solved problem already, and it’s saturated.
Hello lonebhezos, I am wondering if you are sure that CV is saturated because it seems there a still quite a lot of work about algorithm optimization that can be done? It seems that the training time for CV ML model may take days even weeks?
Hahaha. Have you ever heard about CVPR?
Cv and ML. Forget about anything related to hardware.
Even if you believe CV isn't yet solved it will be in 4 years when you finish. Architecture is about researching methods to accelerate such as fp32 vs fp64, distributed model training etc and how that affects ml performance, accuracy etc. The PhD will be responsible for turning those in to.designs such as tensor cores and nvlink. Tons of company's hiring these roles as the cost to produce unique semiconductor designs drops companies are looking for PhDs to help them develop novel computation models to leverage in silicone to have a competitive advantage.
AI/ML is not going to go away for the next 10 years. Very little has been done yet
I think it’s very important to chose what you enjoy for your PhD topic. You will do well in either of them. But there might be more marked demand for the software , algorithm and application aspects of ML than computer architecture. Both are great fields though.