Vitaliy Chiley  is a Machine Learning Research Engineer at the next-generation computing hardware company Cerebras Systems. We spoke about how DL workloads including sparse workloads can run faster on Cerebras hardware.

[00:00:00] Housekeeping

[00:01:08] Preamble

[00:01:50] Vitaliy Chiley Introduction

[00:03:11] Cerebrus architecture

[00:08:12] Memory management and FLOP utilisation

[00:18:01] Centralised vs decentralised compute architecture

[00:21:12] Sparsity

[00:23:47] Does Sparse NN imply Heterogeneous compute?

[00:29:21] Cost of distributed memory stores?

[00:31:01] Activation vs weight sparsity

[00:37:52] What constitutes a dead weight to be pruned?

[00:39:02] Is it still a saving if we have to choose between weight and activation sparsity?

[00:41:02] Cerebras is a cool place to work

[00:44:05] What is sparsity? Why do we need to start dense? 

[00:46:36] Evolutionary algorithms on Cerebras?

[00:47:57] How can we start sparse? Google RIGL

[00:51:44] Inductive priors, why do we need them if we can start sparse?

[00:56:02] Why anthropomorphise inductive priors?

[01:02:13] Could Cerebras run a cyclic computational graph?

[01:03:16] Are NNs locality sensitive hashing tables?

References;

Rigging the Lottery: Making All Tickets Winners [RIGL]

https://arxiv.org/pdf/1911.11134.pdf

[D] DanNet, the CUDA CNN of Dan Ciresan in Jurgen Schmidhuber's team, won 4 image recognition challenges prior to AlexNet

https://www.reddit.com/r/MachineLearning/comments/dwnuwh/d_dannet_the_cuda_cnn_of_dan_ciresan_in_jurgen/ 

A Spline Theory of Deep Learning [Balestriero]

https://proceedings.mlr.press/v80/balestriero18b.html