#ai #sparsity #gpu 




Sparsity is awesome, but only recently has it become possible to properly handle sparse models at good performance. Neural Magic does exactly this, using a plain CPU. No specialized hardware needed, just clever algorithms for pruning and forward-propagation of neural networks. Nir Shavit and I talk about how this is possible, what it means in terms of applications, and why sparsity should play a much larger role in the Deep Learning community.




Sponsor: AssemblyAI


Link: https://www.assemblyai.com/?utm_sourc...




Check out Neural Magic: https://neuralmagic.com/


and DeepSparse: https://github.com/neuralmagic/deepsp...




OUTLINE:


0:00 Introduction


1:08 Sponsor: AssemblyAI


2:50 Start of Interview


4:15 How the NIR company was founded? 


5:10 What is Sparsity about? 


9:30 Link between the human brain and sparsity


12:10 Where should the extra resource that the human brain doesn't have go?


14:40 Analogy for Sparse Architecture


16:48 Possible future for Sparse Architecture as standard architure for Neural Networks


20:08 Pruning & Sparsification


22:57 What keeps us from building sparse models?


25:34 Why are GPUs so unsuited for sparse models?


28:47 CPU and GPU in connection with memory


30:14 What Neural Magic does?


32:54 How do you deal with overlaps in tensor columns?


33:41 The best type of sparsity to execute tons of CPU


37:24 What kind of architecture would make the best use out of a combined system of CPUs and GPUs?


41:04 Graph Neural Networks in connection to sparsity


43:04 Intrinsic connection between the Sparsification of Neural Networks, Non Layer-Wise Computation, Blockchain Technology, Smart Contracts and Distributed Computing


45:23 Neural Magic's target audience


48:16 Is there a type of model where it works particularly well and the type where it doesn't?




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