Looking back now I regret going into HW field. I should have applied to CS rather than ECE, and focus on DL algorithms (especially RL), or maybe even something like what Numenta is doing, because my primary interest is AI, not hardware to run AI.
The hardest part has been being the only DL/ML specialist in my group. Everyone else here is more HW-oriented. I only understood this when I did an internship where I worked with people I could learn from/discuss ideas with.
I looked at your CV, and my guess is that for you, the hardest part will be to focus on one thing for the next 4 years.
>I looked at your CV, and my guess is that for you, the hardest part will be to focus on one thing for the next 4 years.
Is an astute observation although I've found more than enough things in my lab to keep my interest so far^^"
Could you please link or mail me some of your publications? I'd greatly appreciate it
If you like to chat more, send me an email.
Getting production and thermal balancing right is really tough here, and remember that you need scale it to insane production runs to work with IC. But I'd not be surprised to see some breakthroughs in the next few years.
And I think memristives for memory will probably help because they ease the thermal pressure if you manage to eliminate sneak currents, and the material science/microengineering wizards in my lab are working on directly using the vias to make memristives, so obviously I dream of having stacks of memory sandwiching compute layers already...but this is still very much research!
No seriously? These guys here with their DVS: https://inivation.com/
They are already on the market. It's not compute, but it is an awesomely HDR,low latency image sensor which gets you a motion gradient for free. Downside is no colour and no static images, but as a supplementary chip for stabilzation, for robotics or low power "wake up" camera and all those applications it is in my opinion already worth considering, and if the software and algorithmic side of image processing catches up to event stream based sensor data this will be quite awesome.
Disclaimer: I'm acquainted with some of the people who worked on this and think they are all quite lovely people, so might be biased.
At the end, "all" I see happening in the near future will be
* ISA extensions to have accelerators similar to the ones we have for Crypto/RNG and especially vectorization (crossbar matrix multipliers <3 ) * custom chips being made using the standard custom/semicustom design flows, just including memristive and maybe neuromorphic cells * maybe something akin to FPGA programming, especially since intel might be integrating their new Altera into consumer devices https://newsroom.intel.com/news-releases/intel-completes-acq...
If it matters, I graduated with EE but now doing Masters in CS (Machine Learning).
It's basically doing for compute what biomimetics https://en.wikipedia.org/wiki/Biomimetics did already for materials and robots (Festo has some amazing stuff here:https://www.youtube.com/watch?v=7-JvyzOddTM), looking at systems in nature when designing VLSI circuits. Examples would be looking at the eye for camera sensors, the ear for audio, the brain for computation etc.
So it's an orthogonal field that happens to marry nicely with some of the properties of memristives, in that both with them and in the brain there needs to be consideration of noise, false firings etc. It also gets people (like me) excited because it opens up a way for routing and synaptic plasticity which was missing for ML-ASICs so far