Cerebras Systems: Achieving Industry Best AI Performance Through A Systems Approach
An introduction to Cerebras as a company, including a discussion on the core innovations behind the Cerebras CS-1. What is it? How does it work? What does it enable for machine learning practitioners?
April 6, 2021
Generating SIMD Instructions for Cerebras CS-1 using Polyhedral Compilation Techniques
The Cerebras CS-1 is a computing system based on a waferscale processor having nearly 400,000 compute cores. It is intended for training of and inference on deep neural networks.
February 22, 2020
Sven Verdoolaege, Manjunath Kudlur, Rob Schreiber, Harinath Kamepalli
Online Normalization for Training Neural Networks, NeurIps 2019
Online Normalization is a new technique for normalizing the hidden activations of a neural network. Like Batch Normalization, it normalizes the sample dimension. While Online Normalization does not use batches, it is as accurate as Batch Normalization.
May 15, 2019
Vitaliy Chiley, Ilya Sharapov, Atli Kosson, Urs Koster, Ryan Reece, Sofia Samaniego de la Fuente, Vishal Subbiah, Michael James
Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation
We propose combining memory saving techniques with traditional U-Net architectures to increase the complexity of the models on the Brain Tumor Segmentation (BraTS) challenge. The BraTS challenge consists of a 3D segmentation of a 240 240 155 4 input image into a set of tumor classes.
March 5, 2021
Mihir Pendse, Vithursan Thangarasa, Vitaliy Chiley, Ryan Holmdahl, Joel Hestness, Dennis DeCoste