#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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