AbbVie Chooses Cerebras Systems to Accelerate AI Biopharmaceutical Research

Cerebras CS-2 accelerates artificial intelligence work while radically reducing power consumption

Cerebras Systems, the pioneer in high performance artificial intelligence (AI) computing, and AbbVie, a global biopharmaceutical company, announced a landmark achievement in AbbVie’s AI work. Using a Cerebras CS-2 on biomedical natural language processing (NLP) models, AbbVie achieved performance in excess of 128 times that of a graphics processing unit (GPU), while using 1/3 the energy. Not only did AbbVie train the models more quickly, and for less energy, due to the CS-2’s simple, standards-based programming workflow, the time usually allocated to model set up and tuning was also dramatically reduced.

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“The CS-2 system will provide wall-clock improvements that alleviate much of this challenge, while providing a simpler programming model that accelerates our delivery by enabling our teams to iterate more quickly and test more ideas.”

“A common challenge we experience with programming and training BERT _LARGE models is providing sufficient GPU cluster resources for sufficient periods of time,” said Brian Martin, Head of AI at AbbVie. “The CS-2 system will provide wall-clock improvements that alleviate much of this challenge, while providing a simpler programming model that accelerates our delivery by enabling our teams to iterate more quickly and test more ideas.”

With a focus on cutting edge R&D across immunology, neuroscience, oncology, and virology, it’s essential for AbbVie’s scientists to keep abreast of research findings from around the world. To that end, AbbVie employs large, sophisticated AI language models to build its machine translation service, Abbelfish. This service accurately translates and makes searchable vast libraries of biomedical literature across 180 languages using large, state-of-the-art Transformer models such as BERT, BERT _LARGE, and BioBERT.

Ensuring Abbelfish is both accurate and always up to date requires training and re-training the NLP models from scratch with domain-specific biomedical data. However, the Abbelfish model is very large – 6 billion parameters. Such a model is impractical to train on even the largest GPU clusters. Cerebras Systems makes this type of large-scale AI training fast and easy.

Large language models like BERT _LARGE have demonstrated state-of-the-art accuracy on many language processing and understanding tasks. Training these large language models using GPUs is challenging and time-consuming. Training from scratch on new datasets often takes weeks, even on large clusters of legacy equipment. As the size of the cluster grows, power, cost, and complexity grow exponentially. Programming clusters of graphics processing units requires rare skills, different machine learning frameworks and specialized tools that take weeks of engineering time to each iteration.

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The CS-2 was built to directly address these challenges and radically reduce the time to insight. The CS-2 delivers the deep learning performance of 100s of GPUs, with the programming ease of a single node. As a result, less time is spent in set up and configuration, less time is spent training and more ideas are explored. The AbbVie team was able to set up and train their custom BERT _LARGE model from scratch in less than two days with the Cerebras CS-2.

“At Cerebras Systems, our goal is to enable AI that accelerates our customer’s mission,” said Andrew Feldman, CEO and co-founder of Cerebras Systems. “It’s not enough to provide customers with the fastest AI in the market — it also must be the most energy efficient and the easiest to deploy. It’s incredible to see AbbVie not only accelerating their massive language models, but doing so while consuming a fraction of the energy used by legacy solutions.”

The Cerebras CS-2 is powered by the largest processor ever built – the Cerebras Wafer-Scale Engine 2 (WSE-2), which is 56 times larger than the nearest competitor. As a result, the CS-2 delivers more AI-optimized compute cores, more fast memory, and more fabric bandwidth than any other deep learning processor in existence. It was purpose built to accelerate deep learning workloads reducing the time to answer by orders of magnitude.

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