Cerebras Systems Patent – Key Insights and Stats

Cerebras Systems Inc. was founded in 2016 by Andrew Feldman, Jean-Philippe Fricker, Michael James and Sean Lie and is doing business in building computer systems for complex artificial intelligence deep learning applications. As of October 2021, Cerebras Systems has a market valuation of more than $270 million.

Cerebras Systems has a total of 102 patents globally, out of which 34 has been granted. Of these 102 patents, more than 85% patents are active. USA is where Cerebras Systems has filed maximum number of patents, followed by Europe and Japan and it also seems reasonable as the biggest market for Cerebras Systems is the United States, it has generated an annual revenue of $10-$100 million. Parallelly, United States seems to be the main focused R&D center and is also the origin country of Cerebras Systems.

Do read about some of the most popular patents of Cerebras Systems which have been covered by us in this article and also you can find Cerebras system’s patents information, the worldwide patent filing activity and its patent filing trend over the years, and many other stats over Cerebras System’s patent portfolio.

How many patents does Cerebras Systems have?

Cerebras Systems has a total of 102 patents globally. These patents belong to 15 unique patent families. Out of 102 patents, 89 patents are active.

How many Cerebras Systems patents are Alive/Dead?

Worldwide Patents

Cerebras Systems Patent Portfolio

Patent Families

Cerebras Systems Patent

How Many Patents did Cerebras Systems File Every Year?

Cerebras Systems Patent Filing

Are you wondering why there is a drop in patent filing for the last two years? It is because a patent application can take up to 18 months to get published. Certainly, it doesn’t suggest a decrease in the patent filing.

Years of Patent Filing or GrantCerebras Systems Application FiledCerebras Systems Patents Granted
20211014
20202113
2019217
201850

How Many Patents did Cerebras Systems File in Different Countries?

Cerebras Systems Worldwide Patent Filing

Countries in which Cerebras Systems Filed Patents

CountryPatents
United States Of America47
Europe9
Japan9
Canada8
China2
Korea (South)1
Hong Kong (S.A.R.)1

Where are Research Centers of Cerebras Systems Patents Located?

Cerebras Systems R&D Center

10 Best Cerebras Systems Patents

US20200005142A1 is the most popular patent in the Cerebras Systems portfolio. It has received 8 citations so far from companies like Sony Corp, Intel Corporation and International Business Machines Corporation.

Publication NumberCitation Count
US20200005142A18
WO2018193377A18
WO2018193360A18
WO2018193354A18
US10515303B27
WO2020021395A17
WO2018193380A17
WO2018193379A17
WO2018193370A17
WO2018193363A17

What Percentage of Cerebras Systems US Patent Applications were Granted?

Cerebras Systems (Excluding its subsidiaries) has filed 37 patent applications at USPTO so far (Excluding Design and PCT applications). Out of these 22 have been granted leading to a grant rate of 100%.

Which Law Firms Filed Most US Patents for Cerebras Systems?

Law FirmTotal ApplicationSuccess Rate
Schox Pc24100%
Patentventures Cs13100%

“We have come together to build a new class of computer to accelerate artificial intelligence work by three orders of magnitude beyond the current state of the art.”

Cerebras Systems is the pioneer in high performance artificial intelligence computing and Peptilogics. It has been named to Fast Company’s prestigious annual list of the World’s Most Innovative Companies for 2021.

What are Cerebras Systems’s key innovation segments? What can you expect next??

List of Cerebras Systems Patents

Publication NumberTitle (English)
EP3607506B1Fabric vectors for deep learning acceleration
US11062202B2Numerical representation for neural networks
US11062200B2Task synchronization for accelerated deep learning
CN110869946BAccelerated deep learning
JP06860694B2The task activation of acceleration deep learning
JP06854473B2The data-flow trigger task of acceleration deep learning
US10971401B2Systems and methods for precision fabrication of an orifice within an integrated circuit
US10957595B2Systems and methods for precision fabrication of an orifice within an integrated circuit
CA3060356CTask activating for accelerated deep learning
CA3051990CAccelerated deep learning
JP06832050B2Acceleration deep learning
US10923456B2Systems and methods for hierarchical exposure of an integrated circuit having multiple interconnected die
US10923412B2Apparatuses and methods for implementing a sliding thermal interface between substrates with varying coefficients of thermal expansion
US10892244B2Apparatus and method for securing substrates with varying coefficients of thermal expansion
CA3060969CNeuron smearing for accelerated deep learning
US10840216B2Systems and methods for powering an integrated circuit having multiple interconnected die
US10784128B2Apparatus and method for securing components of an integrated circuit
JP06755541B2Neuron smearing of acceleration deep learning
US10777532B2Apparatus and method for multi-die interconnection
US10762418B2Control wavelet for accelerated deep learning
US10726329B2Data structure descriptors for deep learning acceleration
CA3060368CDataflow triggered tasks for accelerated deep learning
US10699189B2Accelerated deep learning
US10672732B2Apparatus and method for securing substrates with varying coefficients of thermal expansion
US10657438B2Backpressure for accelerated deep learning
US10614357B2Dataflow triggered tasks for accelerated deep learning
US10586784B2Apparatus and method for multi-die interconnection
US10515303B2Wavelet representation for accelerated deep learning
US10468369B2Apparatus and method for securing substrates with varying coefficients of thermal expansion
US10453717B2Apparatus and method for securing components of an integrated circuit
US10366967B2Apparatus and method for multi-die interconnection
US10361172B2Apparatus and method for multi-die interconnection
US10332860B2Apparatus and method for multi-die interconnection
US10242891B2Apparatus and method for securing components of an integrated circuit
US20210256362A1Processor element redundancy for accelerated deep learning
US20210255860A1Isa enhancements for accelerated deep learning
US20210248453A1Scaled compute fabric for accelerated deep learning
JP2021108157ATask activating for accelerated deep learning
JP2021108131ANeuron smearing for accelerated deep learning
US20210224639A1Control wavelet for accelerated deep learning
US20210167037A1Systems and methods for hierarchical exposure of an integrated circuit having multiple interconnected die
US20210166109A1Data structure descriptors for deep learning acceleration
JP2021082317AAcceleration deep learning
EP3659178A4Apparatus and method for securing substrates with varying coefficients of thermal expansion
US20210143041A1System and method for alignment of an integrated circuit
US20210142167A1Accelerated deep learning
US20210134700A1Apparatuses and methods for implementing a sliding thermal interface between substrates with varying coefficients of thermal expansion
WO2021084506A1Distributed placement of linear operators for accelerated deep learning
WO2021084505A1Optimized placement for efficiency for accelerated deep learning
WO2021084485A1Placement of compute and memory for accelerated deep learning
US20210125871A1Systems and methods for precision fabrication of an orifice within an integrated circuit
EP3659055A4Apparatus and method for multi-die interconnection
WO2021074867A1Advanced wavelet filtering for accelerated deep learning
WO2021074865A1Basic wavelet filtering for accelerated deep learning
WO2021074795A1Dynamic routing for accelerated deep learning
US20210097376A1Backpressure for Accelerated Deep Learning
US20210091035A1Apparatus and method for securing substrates with varying coefficients of thermal expansion
EP3610424A4Data structure descriptors for deep learning acceleration
US20210056400A1Dataflow Triggered Tasks for Accelerated Deep Learning
KR2021014056AAccelerated deep learning
US20210004674A1Task activating for accelerated deep learning
US20200402957A1Systems and methods for powering an integrated circuit having multiple interconnected die
JP2020205067ANeuron smearing for accelerated Deep Learning | Neuron smearing of acceleration deep learning
US20200381394A1Apparatus and method for multi-die interconnection
US20200381274A1Apparatus and method for securing components of an integrated circuit
US20200380370A1Floating-point unit stochastic rounding for accelerated deep learning
US20200380344A1Neuron smearing for accelerated deep learning
US20200380341A1Fabric Vectors for Deep Learning Acceleration
US20200364546A1Wavelet representation for accelerated deep learning
EP3610612A4Dataflow triggered tasks for accelerated deep learning
HK40016392AAccelerated deep learning
JP2020517030ANeuron smearing for accelerated deep learning
CN111095527ADevice and method for multi-die interconnection
EP3607505A4Task synchronization for accelerated deep learning
EP3607503A4Task activating for accelerated deep learning
US20200125934A1Microthreading for accelerated deep learning
EP3607504A4Neuron smearing for accelerated deep learning
WO2020044238A1Processor element redundancy for accelerated deep learning
WO2020044208A1Isa enhancements for accelerated deep learning
WO2020044152A1Scaled compute fabric for accelerated deep learning
WO2020021395A1Numerical representation for neural networks
EP3563307A4Accelerated deep learning
US20200005142A1Accelerated deep learning
WO2019040273A1Apparatus and method for securing components of an integrated circuit
WO2019022942A1Apparatus and method for securing substrates with varying coefficients of thermal expansion
WO2019022902A1Apparatus and method for multi-die interconnection
WO2018193380A1Fabric vectors for deep learning acceleration
WO2018193379A1Backpressure for accelerated deep learning
WO2018193377A1Control wavelet for accelerated deep learning
WO2018193370A1Task activating for accelerated deep learning
WO2018193363A1Data structure descriptors for deep learning acceleration
WO2018193361A1Microthreading for accelerated deep learning
WO2018193360A1Task synchronization for accelerated deep learning
WO2018193354A1Wavelet representation for accelerated deep learning
WO2018193353A1Neuron smearing for accelerated deep learning
WO2018193352A1Dataflow triggered tasks for accelerated deep learning
CA3108089A1Task activating for accelerated deep learning
CA3099965A1Neuron smearing for accelerated deep learning
CA3060350A1Data structure descriptors for deep learning acceleration
WO2018189728A1Floating-point unit stochastic rounding for accelerated deep learning
WO2018154494A1Accelerated deep learning
CA3108151A1Accelerated deep learning
Updated on November 29, 2021

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