#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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