Key Enabling Technologies

Wafer-Scale Integration

Small-chip accelerators are limited by slow off-chip DRAM to 2 megapixels. With 40GB of on-chip SRAM, Cerebras handles 25 megapixels with ease.

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Weight Streaming

The key to large image capability, weight streaming allows us to stream layers into the CS-2 system without the penalty of off-chip memory.

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Easy Model Size Changes

To easily leverage increased data sizes, make simple changes to both simple model depth and width in PyTorch or configuration files.

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50 Megapixel Segmentation

Distributed memory with massive bandwidth enables segmentation training on multi-channel inputs up to 50 megapixels with deep and shallow networks.

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Ultra-Simple Programming

Effortlessly train deep, wide classification and segmentation CV models on Cerebras systems without parallel programming using familiar frameworks.

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Featured Resources

More Pixels, More Context, More Insight!

The Cerebras architecture overcomes the limitations of GPUs to train large computer vision models on high-resolution, 50 megapixel images.

Unlocking High-Resolution Computer Vision with WSI

A platform for accelerating CV workloads that allows users to easily scale CNN-based models to large images and volumes.

UNet Image Segmentation Walkthrough

See how easy it is to train different resolution and depth UNet models by changing a few configuration parameters.

Bigger Images, Higher Accuracy

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