Health & Pharma

Cerebras accelerates AI to drive faster drug discovery and enable more accurate healthcare recommendations based on vastly larger datasets.

Industry Challenge:

AI has industry-changing potential to accelerate biomedical research and drug discovery, and improve data-driven healthcare recommendations.

But these complex problems need large, complex models to represent them, and datasets in this space are enormous. These experiments today take too long to train and are too slow in inference. Traditional hardware limits how quickly researchers can innovate and constrains the image resolution and sequence lengths they can use.

CS-2 offers cluster-scale AI acceleration in a single, easy-to-program device, so your researchers can focus on medical innovation; not on overcoming speed and scaling challenges.

Use Case

Drug discovery

Traditional laboratory-based drug discovery takes years from compound research into trials. AI models like Transformers and Graph Neural Networks (GNNs) have the potential to enable faster development by helping to predict candidate compounds’ biological behavior and treatment efficacy.

CS-2 was built for this type of work. It can train models like these and run inference hundreds of times faster than the competition, enabling research insights in months rather than years.

Use Case

Text and language modeling

Neural networks like BERT and GPT can model semantic relationships within records, reports, and scientific literature, so you can instantly answer questions using this database of knowledge.

Today, the compute resources and expertise needed to efficiently work with large language models – such as BERT and GPT – and massive real-world text databases are only available in hyperscale datacenters. With a single CS-2, your organization can train models like these in hours or days rather than weeks or months.

Use Case

Genomics and data science

In genomics, AI has shown great potential for identifying subtle signatures of public health challenges as well as new opportunities for the treatment of rare diseases.

However, most work in this space has been limited to small clinical trials or local populations because the deep learning models used to classify sequences or predict phenotype — e.g. RNNs, Transformers, 1D CNNs — take too long to train or process with large, sparse datasets on GPU. Use the CS-2 to bring 100x – 1,000x more data to your models.

Use Case

Care and treatment analytics

Knowledge graphs and graph neural networks can be used to represent the complex landscape of patients – treatments, outcomes, costs – and to recommend better data-driven courses of care.

While this potential is great, the inputs are enormous — large, sparse datasets of health records and reports, and massive patient population graphs.

With 850,000 cores, 40 GB of fast on-wafer SRAM memory, and a super-fast on-wafer communication fabric, Cerebras’ WSE-2 supports larger graphs at orders of magnitude greater performance and efficiency than a cluster of traditional machines. 

At GSK, we are applying machine learning to make better predictions in drug discovery, so we are amassing data – faster than ever before...With the Cerebras CS-1, we have been able to increase the complexity of the encoder models that we can generate, while decreasing their training time by 80x.

Kim Branson

SVP Global Head of AI and ML @ GlaxoSmithKline