#77 - Vitaliy Chiley (Cerebras)
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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
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