4 ms·
I am guessing that groq did the wrong thing here. To my eyes, deep learning asics generally are only meaningful in 2 separate scenarios: a high power high scal
by justicezyx 6y ago
I am guessing that groq did the wrong thing here.
To my eyes, deep learning asics generally are only meaningful in 2 separate scenarios: a high power high scale data center training chip; or a low power highly efficient edge inference chip.
TSP appears a throughput oriented high power inference chip. I don't know any decent size market can support such chip from a start-up.
- ganzuul 6y agoDrone / smartphone SLAM? I expected it years ago but have not yet seen it implemented in any kind of scale.
- justicezyx 6y agoSmartphone emphasize efficiency. Most of them are computer vision that Convolution-based algorithms is powerful enough to be the general algorithm that any asic vendors can target. And that is also a highly mature and established market. They often have similar architecture in terms of the execution workflow, but added NN-flavored instruction units and instructions. That drives down cost and makes them easier to program with. Drone, they are more energy limited than smartphone, as the propulsion system consumes more energy. Inference throughput seems a secondary problem to drones.