Cerebras overtakes peers as #1 AI Semiconductor Startup In the freshly-updated 2023 State of AI Report Compute Index from Air Street Capital and Zeta Alpha, Cerebras is highlighted for leading all AI semiconductor startups in research publication counts, open source
@cerebras
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Cerebras Sponsors Supercomputing Asia 2024 Beyond Exascale
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Cerebras is proud to be a sponsor of Supercomputing Asia 2024 Please join our SVP of Product & Strategy, Andy Hock, for his talk "Going big: unlocking #AI and #HPC beyond exascale with Cerebras" on February 20th at 4 pm local time. We'll also host a dinner for select
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KAUST and Cerebras Named Finalists for 2023 Gordon Bell Prize
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We are honored to have met KAUST's President, Dr. Tony F Chan, and to work on revolutionary research together. At #SC23, King Abdullah University of Science and Technology (KAUST) and Cerebras Systems were finalists for the 2023 Gordon Bell Prize, the most prestigious award for
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Modular Library Advances Sparse Machine Learning Research
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(9/n) Our library is modular and extensible, enabling users to define algorithms without complex rewrites. It is already pivotal in supporting in-house research on sparsity, demonstrated through our works: *Sparse-IFT: https://
openreview.net/pdf?id=iP4WcJ4
EX0
…, and https://
proceedings.mlr.press/v216/thangaras
a23a.html
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Cerebras Sparsity Optimization for Foundation Model Training
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(10/n) Contact us to learn more about Cerebras and how sparsity can make training your next foundation model orders of magnitude more efficient. Shoutout to the amazing software, machine learning, and performance team members who’ve played an instrumental role in developing
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RigL Dynamic Weight Sparsity Algorithm Implementation PyTorch
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(8/n) These allow us to implement popular dynamic weight sparsity algorithms such as Rigging the Lottery (RigL) from @GoogleDeepMind
. Enabling these algorithms for any existing PyTorch model in our ModelZoo is as simple as making a few changes in our configuration file. -
Flexible Sparsity Distributions for Advanced Model Optimization
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(7/n) We provide a lot of flexibility, including custom sparsity distributions, schedules for sparsity, and other richer features such as grouped tensor views and tie-breaking mechanisms.
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PyTorch Sparse Optimizers for Efficient Model Training
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(6/n) We rely on PyTorch’s optimizer interface to design all our sparse algorithms as special optimizers, which operate on a sparse view of the model parameters and gradients during training. We use a wrapper over existing PyTorch optimizers, which enables efficient
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Cerebras CS-2 Accelerates Foundation Model Training Through Sparsity
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(5/n) Our library is hardware agnostic, but combined with the Cerebras CS-2's unique ability to accelerate unstructured #sparsity, it unleashes unparalleled efficiency in training foundation models.
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Library Enables Dense and Sparse AI Training Workloads
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(4/n) Our library has been co-designed from the ground up to run both dense and #sparse training workloads. This enables practitioners to focus on what they do best: push the boundaries of AI research.