4 ms·
Hardware for deep learning
- fear91 12y agoHow does this compare to CUDA implementations?
- p1esk 12y agoA better question would be how it will compare to CUDA implementations when it's released? Moore's law killed all such initiatives in the past, however, perhaps this time it will be different?
- 3rd3 12y agoThere is similar research (namely ASICs for machine learning) going on at Stanford, but there it’s cast in analog circuitry: http://phys.org/news/2014-04-scientists-circuit-board-human-brain.html http://phys.org/news/2014-04-scientists-circuit-board-human-...
- ArtificialLearn 12y agoThe Stanford chips are biomimetic, trying to copy how the brain is thought to work. Artificial Learning's are machine-learning theoretic, embodying proven ML algorithms in hardware.
- 3rd3 12y agoAh, thanks for the clarification.
- jasonlaramburu 12y agoIs there are real advantage to have this running on the device? Why not use lower profile hardware like electric imp and run everything in the cloud?
- p1esk 12y agoNot if you want to use it in autonomous drones...
- ArtificialLearn 12y agoArtificial Learning's largest designs are 180nm tech so some way from Moore's Law limits. Efficiency advantage over GPGPU implementation calculated as better than 10,000 fold in terms of learning updates per second per watt per kg.