Cerebras

Cerebras was founded in 2015 by Andrew Feldman, Gary Lauterbach, Michael James, Sean Lie and Jean-Philippe Fricker.[3] These five founders worked together at SeaMicro, which was started in 2007 by Feldman and Lauterbach and was later sold to AMD in 2012 for $334 million.[4][5]

In May 2016, Cerebras secured $27 million in series A funding led by Benchmark, Foundation Capital and Eclipse Ventures.[6][3]

In December 2016, series B funding was led by Coatue, followed in January 2017 with series C funding led by VY Capital.[3]

In November 2018, Cerebras closed its series D round with $88 million, making the company a unicorn. Investors in this round included Altimeter, VY Capital, Coatue, Foundation Capital, Benchmark, and Eclipse.[7][8]

On August 19, 2019, Cerebras announced its Wafer-Scale Engine (WSE).[9][10][11]

In November 2019, Cerebras closed its series E round with over $270 million for a valuation of $2.4 billion.[12]

In 2020, the company announced an office in Japan and partnership with Tokyo Electron Devices.[13]

In April 2021, Cerebras announced the CS-2 based on the company's Wafer Scale Engine Two (WSE-2), which has 850,000 cores.[1] In August 2021, the company announced its brain-scale technology that can run a neural network with over 120 trillion connections.[14]

In November 2021, Cerebras announced that it had raised an additional $250 million in Series F funding, valuing the company at over $4 billion. The Series F financing round was led by Alpha Wave Ventures and Abu Dhabi Growth Fund (ADG).[15] To date, the company has raised $720 million in financing.[15][16]

The Cerebras Wafer Scale Engine (WSE) is a single, wafer-scale integrated processor that includes compute, memory and interconnect fabric. The WSE-1 powers the Cerebras CS-1, which is Cerebras’ first-generation AI computer.[17] It is a 19-inch rack-mounted appliance designed for AI training and inference workloads in a datacenter.[10] The CS-1 includes a single WSE primary processor with 400,000 processing cores, as well as twelve 100 Gigabit Ethernet connections to move data in and out.[18][10] The WSE-1 has 1.2 trillion transistors, 400,000 compute cores and 18 gigabytes of memory.[9][10][11]

In April 2021, Cerebras announced the CS-2 AI system based on the 2nd-generation Wafer Scale Engine (WSE-2), manufactured by the 7 nm process of TSMC .[1] It is 26 inches tall and fits in one-third of a standard data center rack.[19][1] The Cerebras WSE-2 has 850,000 cores and 2.6 trillion transistors.[19][20] The WSE-2 expanded on-chip SRAM to 40 gigabytes, memory bandwidth to 20 petabytes per second and total fabric bandwidth to 220 petabits per second.[21][22]

In August 2021, the company announced a system which connects multiple integrated circuits (commonly called "chips") into a neural network with many connections. [14] It enables a single system to support AI models with more than 120 trillion parameters.[23]

Customers are reportedly using Cerebras technologies in the pharmaceutical, life sciences, and energy sectors.[24][25]

In 2020, GlaxoSmithKline (GSK) began using the Cerebras CS-1 AI system in their London AI hub, for neural network models to accelerate genetic and genomic research and reduce the time taken in drug discovery.[26] The GSK research team was able to increase the complexity of the encoder models they could generate, while reducing training time.[27] Other pharmaceutical industry customers include AstraZeneca, who was able to reduce training time from two weeks on a cluster of GPUs to two days using the Cerebras CS-1 system.[28] GSK and Cerebras recently co-published research in December 2021 on epigenomic language models.

Argonne National Laboratory has been using the CS-1 since 2020 in COVID-19 research and cancer tumor research based on the world’s largest cancer treatment database.[29] A series of models running on the CS-1 to predict cancer drug response to tumors achieved speed-ups of many hundreds of times on the CS-1 compared to their GPU baselines.[24]

Cerebras and the National Energy Technology Laboratory (NETL) demonstrated record-breaking performance of Cerebras' CS-1 system on a scientific compute workload in November 2020. The CS-1 was 200 times faster than the Joule Supercomputer on the key workload of Computational Fluid Dynamics.[30]

The Lawrence Livermore National Lab’s Lassen supercomputer incorporated the CS-1 in both classified and non-classified areas for physics simulations.[31] The Pittsburgh Supercomputing Center (PSC) has also incorporated the CS-1 in their Neocortex supercomputer for dual HPC and AI workloads.[32] EPCC, the supercomputing center of the University of Edinburgh, has also deployed a CS-1 system for AI-based research.[33]

In August 2021, Cerebras announced a partnership with Peptilogics on the development of AI for peptide therapeutics.[34]

In March 2022, Cerebras announced that the Company deployed its CS-2 system in the Houston facilities of TotalEnergies, its first publicly disclosed customer in the energy sector.[25] Cerebras also announced that it has deployed a CS-2 system at nference, a startup that uses natural language processing to analyze massive amounts of biomedical data. The CS-2 will be used to train transformer models that are designed to process information from piles of unstructured medical data to provide fresh insights to doctors and improve patient recovery and treatment.[35]

• Wafer-scale integration
• Wafer-level packaging
• Semiconductor device fabrication
• Transistor count
• Deep learning processor

• Official website