Mentium Technologies Inc.
Dba Mentium Technologies Inc
CAGE Code: 7UZU7
NCAGE Code: 7UZU7
Status: Active
Type: Commercial Supplier
Summary
Mentium Technologies Inc., Dba Mentium Technologies Inc is an Active Commercial Supplier with the Cage Code 7UZU7.
Address
3448 Elings Hall
Santa Barbara CA 93106-0001
United States
Points of Contact
- Telephone:
- 8056176245
Related Information
No Related Information...
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Frequently Asked Questions (FAQ) for CAGE 7UZU7
- What is CAGE Code 7UZU7?
- 7UZU7 is the unique identifier used by NATO Organizations to reference the physical entity known as Mentium Technologies Inc. Dba Mentium Technologies Inc located at 3448 Elings Hall, Santa Barbara CA 93106-0001, United States.
- Who is CAGE Code 7UZU7?
- 7UZU7 refers to Mentium Technologies Inc. Dba Mentium Technologies Inc located at 3448 Elings Hall, Santa Barbara CA 93106-0001, United States.
- Where is CAGE Code 7UZU7 Located?
- CAGE Code 7UZU7 is located in Santa Barbara, CA, USA.
Contracting History for CAGE 7UZU7 Most Recent 25 Records
- 80NSSC23CA207
- Fy23 Sbir Phase Iii - Testing Neuromorphic Architectures For High Capacity/Low-Power Ai In Sub-Orbital Flight
- 20 Sep 2023
- Fy23 Sbir Phase Iii - Testing Neuromorphic Architectures For High Capacity/Low-Power Ai In Sub-Orbital Flight
- Nasa Shared Services Center
- National Aeronautics And Space Administration (Nasa)
- $439,546.00
- National Aeronautics And Space Administration (Nasa)
- 80NSSC22CA230
- Eo14042 Sbir Phase Ii Lunar Sequential - Neuromorphic Chip For Sensing, Situational Awarness, And Decision Making In Radiative Environments
- 28 Sep 2022
- Eo14042 Sbir Phase Ii Lunar Sequential - Neuromorphic Chip For Sensing, Situational Awarness, And Decision Making In Radiative Environments
- Nasa Shared Services Center
- National Aeronautics And Space Administration (Nasa)
- $4,567,449.00
- National Aeronautics And Space Administration (Nasa)
- 80NSSC18C0094
- Eo14042 Artificial Intelligence Realized Through Machine Learning Algorithms Seems To Be The Only Viable Solution To Implement Perception, Enable Pilot Assistants And Eventually Full Autonomy To Uas. Currently, Many Uas Have Some Kind Of Conventional Computer Vision (Cv) Helping Them In Obstacle Avoidance Or Target Acquisition. Interestingly Though, Since 2012 Deep Neural Networks (Dnn) Have Dramatically Outperformed Conventional Cv Algorithms In Those Tasks And Pushed Artificial Intelligence (Ai) Limits In A Variety Of Other Applications Including, But Not Limited, To Object Recognition, Video Analytics, Decision Making And Control, Speech Recognition, Etc. Unfortunately, The Computational Power Required For Real-Time Dnn Operation Can Still Only Be Delivered By Bulky, Expensive, Slow, Heavy And Energy-Hungry Digital Systems Like Gpus. This Is Why Mentium Is Devoted To Delivering Disruptive Technology In The Field Of Machine Learning Hardware Accelerators, And In Particular For This Project, Into The Deep Learning Hardware Accelerators Field. Experimental Data And Phase I Results That Our Hardware Can Deliver 100X To 1000X Gain In Speed And In Power Efficiency Compared To Other Stateof- The-Art Accelerators. Our Final Product Will Be Able To Analyze, In Real-Time, Big Data Streams Coming From Cameras, Sensors And/Or Avionics And To Categorize (Classify) Them For The Purpose Of Decision Making Or Object Localization To Achieve Better Navigation And Collision Avoidance In Uas. The Same Hardware Processor Will Be Deployable In The Air Traffic Systems, For Real-Time Data Analysis And Decision-Making. All With More Than 10X Reduction In Cost And Power Consumption. This Distruptive Technology Is Based On An Analogcomputational Core, Exploiting The Memory Devices To Carry Out The Computation At A Physical Level. Analog Computation Is Inherently Faster And More Efficient Than The Digital One, While The In-Memory Computation Removes The Data Transfer Bottleneck.
- 8 Oct 2021
- Eo14042 Artificial Intelligence Realized Through Machine Learning Algorithms Seems To Be The Only Viable Solution To Implement Perception, Enable Pilot Assistants And Eventually Full Autonomy To Uas. Currently, Many Uas Have Some Kind Of Conventional Computer Vision (Cv) Helping Them In Obstacle Avoidance Or Target Acquisition. Interestingly Though, Since 2012 Deep Neural Networks (Dnn) Have Dramatically Outperformed Conventional Cv Algorithms In Those Tasks And Pushed Artificial Intelligence (Ai) Limits In A Variety Of Other Applications Including, But Not Limited, To Object Recognition, Video Analytics, Decision Making And Control, Speech Recognition, Etc. Unfortunately, The Computational Power Required For Real-Time Dnn Operation Can Still Only Be Delivered By Bulky, Expensive, Slow, Heavy And Energy-Hungry Digital Systems Like Gpus. This Is Why Mentium Is Devoted To Delivering Disruptive Technology In The Field Of Machine Learning Hardware Accelerators, And In Particular For This Project, Into The Deep Learning Hardware Accelerators Field. Experimental Data And Phase I Results That Our Hardware Can Deliver 100X To 1000X Gain In Speed And In Power Efficiency Compared To Other Stateof- The-Art Accelerators. Our Final Product Will Be Able To Analyze, In Real-Time, Big Data Streams Coming From Cameras, Sensors And/Or Avionics And To Categorize (Classify) Them For The Purpose Of Decision Making Or Object Localization To Achieve Better Navigation And Collision Avoidance In Uas. The Same Hardware Processor Will Be Deployable In The Air Traffic Systems, For Real-Time Data Analysis And Decision-Making. All With More Than 10X Reduction In Cost And Power Consumption. This Distruptive Technology Is Based On An Analogcomputational Core, Exploiting The Memory Devices To Carry Out The Computation At A Physical Level. Analog Computation Is Inherently Faster And More Efficient Than The Digital One, While The In-Memory Computation Removes The Data Transfer Bottleneck.
- Nasa Shared Services Center
- National Aeronautics And Space Administration (Nasa)
- $1,129,356.00
- National Aeronautics And Space Administration (Nasa)
- 80NSSC18C0094
- Eo14042 Artificial Intelligence Realized Through Machine Learning Algorithms Seems To Be The Only Viable Solution To Implement Perception, Enable Pilot Assistants And Eventually Full Autonomy To Uas. Currently, Many Uas Have Some Kind Of Conventional Computer Vision (Cv) Helping Them In Obstacle Avoidance Or Target Acquisition. Interestingly Though, Since 2012 Deep Neural Networks (Dnn) Have Dramatically Outperformed Conventional Cv Algorithms In Those Tasks And Pushed Artificial Intelligence (Ai) Limits In A Variety Of Other Applications Including, But Not Limited, To Object Recognition, Video Analytics, Decision Making And Control, Speech Recognition, Etc. Unfortunately, The Computational Power Required For Real-Time Dnn Operation Can Still Only Be Delivered By Bulky, Expensive, Slow, Heavy And Energy-Hungry Digital Systems Like Gpus. This Is Why Mentium Is Devoted To Delivering Disruptive Technology In The Field Of Machine Learning Hardware Accelerators, And In Particular For This Project, Into The Deep Learning Hardware Accelerators Field. Experimental Data And Phase I Results That Our Hardware Can Deliver 100X To 1000X Gain In Speed And In Power Efficiency Compared To Other Stateof- The-Art Accelerators. Our Final Product Will Be Able To Analyze, In Real-Time, Big Data Streams Coming From Cameras, Sensors And/Or Avionics And To Categorize (Classify) Them For The Purpose Of Decision Making Or Object Localization To Achieve Better Navigation And Collision Avoidance In Uas. The Same Hardware Processor Will Be Deployable In The Air Traffic Systems, For Real-Time Data Analysis And Decision-Making. All With More Than 10X Reduction In Cost And Power Consumption. This Distruptive Technology Is Based On An Analogcomputational Core, Exploiting The Memory Devices To Carry Out The Computation At A Physical Level. Analog Computation Is Inherently Faster And More Efficient Than The Digital One, While The In-Memory Computation Removes The Data Transfer Bottleneck.
- 24 Mar 2022
- Eo14042 Artificial Intelligence Realized Through Machine Learning Algorithms Seems To Be The Only Viable Solution To Implement Perception, Enable Pilot Assistants And Eventually Full Autonomy To Uas. Currently, Many Uas Have Some Kind Of Conventional Computer Vision (Cv) Helping Them In Obstacle Avoidance Or Target Acquisition. Interestingly Though, Since 2012 Deep Neural Networks (Dnn) Have Dramatically Outperformed Conventional Cv Algorithms In Those Tasks And Pushed Artificial Intelligence (Ai) Limits In A Variety Of Other Applications Including, But Not Limited, To Object Recognition, Video Analytics, Decision Making And Control, Speech Recognition, Etc. Unfortunately, The Computational Power Required For Real-Time Dnn Operation Can Still Only Be Delivered By Bulky, Expensive, Slow, Heavy And Energy-Hungry Digital Systems Like Gpus. This Is Why Mentium Is Devoted To Delivering Disruptive Technology In The Field Of Machine Learning Hardware Accelerators, And In Particular For This Project, Into The Deep Learning Hardware Accelerators Field. Experimental Data And Phase I Results That Our Hardware Can Deliver 100X To 1000X Gain In Speed And In Power Efficiency Compared To Other Stateof- The-Art Accelerators. Our Final Product Will Be Able To Analyze, In Real-Time, Big Data Streams Coming From Cameras, Sensors And/Or Avionics And To Categorize (Classify) Them For The Purpose Of Decision Making Or Object Localization To Achieve Better Navigation And Collision Avoidance In Uas. The Same Hardware Processor Will Be Deployable In The Air Traffic Systems, For Real-Time Data Analysis And Decision-Making. All With More Than 10X Reduction In Cost And Power Consumption. This Distruptive Technology Is Based On An Analogcomputational Core, Exploiting The Memory Devices To Carry Out The Computation At A Physical Level. Analog Computation Is Inherently Faster And More Efficient Than The Digital One, While The In-Memory Computation Removes The Data Transfer Bottleneck.
- Nasa Shared Services Center
- National Aeronautics And Space Administration (Nasa)
- $1,129,356.00
- National Aeronautics And Space Administration (Nasa)
- 80NSSC20C0682
- Radiation Hardened In-Memory Computing For Space Applications
- 22 Sep 2021
- Radiation Hardened In-Memory Computing For Space Applications
- Nasa Shared Services Center
- National Aeronautics And Space Administration (Nasa)
- $181,000.00
- National Aeronautics And Space Administration (Nasa)
- 80NSSC18C0094
- Eo14042 Artificial Intelligence Realized Through Machine Learning Algorithms Seems To Be The Only Viable Solution To Implement Perception, Enable Pilot Assistants And Eventually Full Autonomy To Uas. Currently, Many Uas Have Some Kind Of Conventional Computer Vision (Cv) Helping Them In Obstacle Avoidance Or Target Acquisition. Interestingly Though, Since 2012 Deep Neural Networks (Dnn) Have Dramatically Outperformed Conventional Cv Algorithms In Those Tasks And Pushed Artificial Intelligence (Ai) Limits In A Variety Of Other Applications Including, But Not Limited, To Object Recognition, Video Analytics, Decision Making And Control, Speech Recognition, Etc. Unfortunately, The Computational Power Required For Real-Time Dnn Operation Can Still Only Be Delivered By Bulky, Expensive, Slow, Heavy And Energy-Hungry Digital Systems Like Gpus. This Is Why Mentium Is Devoted To Delivering Disruptive Technology In The Field Of Machine Learning Hardware Accelerators, And In Particular For This Project, Into The Deep Learning Hardware Accelerators Field. Experimental Data And Phase I Results That Our Hardware Can Deliver 100X To 1000X Gain In Speed And In Power Efficiency Compared To Other Stateof- The-Art Accelerators. Our Final Product Will Be Able To Analyze, In Real-Time, Big Data Streams Coming From Cameras, Sensors And/Or Avionics And To Categorize (Classify) Them For The Purpose Of Decision Making Or Object Localization To Achieve Better Navigation And Collision Avoidance In Uas. The Same Hardware Processor Will Be Deployable In The Air Traffic Systems, For Real-Time Data Analysis And Decision-Making. All With More Than 10X Reduction In Cost And Power Consumption. This Distruptive Technology Is Based On An Analogcomputational Core, Exploiting The Memory Devices To Carry Out The Computation At A Physical Level. Analog Computation Is Inherently Faster And More Efficient Than The Digital One, While The In-Memory Computation Removes The Data Transfer Bottleneck.
- 21 Jun 2022
- Eo14042 Artificial Intelligence Realized Through Machine Learning Algorithms Seems To Be The Only Viable Solution To Implement Perception, Enable Pilot Assistants And Eventually Full Autonomy To Uas. Currently, Many Uas Have Some Kind Of Conventional Computer Vision (Cv) Helping Them In Obstacle Avoidance Or Target Acquisition. Interestingly Though, Since 2012 Deep Neural Networks (Dnn) Have Dramatically Outperformed Conventional Cv Algorithms In Those Tasks And Pushed Artificial Intelligence (Ai) Limits In A Variety Of Other Applications Including, But Not Limited, To Object Recognition, Video Analytics, Decision Making And Control, Speech Recognition, Etc. Unfortunately, The Computational Power Required For Real-Time Dnn Operation Can Still Only Be Delivered By Bulky, Expensive, Slow, Heavy And Energy-Hungry Digital Systems Like Gpus. This Is Why Mentium Is Devoted To Delivering Disruptive Technology In The Field Of Machine Learning Hardware Accelerators, And In Particular For This Project, Into The Deep Learning Hardware Accelerators Field. Experimental Data And Phase I Results That Our Hardware Can Deliver 100X To 1000X Gain In Speed And In Power Efficiency Compared To Other Stateof- The-Art Accelerators. Our Final Product Will Be Able To Analyze, In Real-Time, Big Data Streams Coming From Cameras, Sensors And/Or Avionics And To Categorize (Classify) Them For The Purpose Of Decision Making Or Object Localization To Achieve Better Navigation And Collision Avoidance In Uas. The Same Hardware Processor Will Be Deployable In The Air Traffic Systems, For Real-Time Data Analysis And Decision-Making. All With More Than 10X Reduction In Cost And Power Consumption. This Distruptive Technology Is Based On An Analogcomputational Core, Exploiting The Memory Devices To Carry Out The Computation At A Physical Level. Analog Computation Is Inherently Faster And More Efficient Than The Digital One, While The In-Memory Computation Removes The Data Transfer Bottleneck.
- Nasa Shared Services Center
- National Aeronautics And Space Administration (Nasa)
- $1,129,356.00
- National Aeronautics And Space Administration (Nasa)
- 80NSSC20C0682
- E014042 Radiation Hardened In-Memory Computing For Space Applications
- 24 Mar 2022
- E014042 Radiation Hardened In-Memory Computing For Space Applications
- Nasa Shared Services Center
- National Aeronautics And Space Administration (Nasa)
- $181,000.00
- National Aeronautics And Space Administration (Nasa)
- 80NSSC18C0094
- Artificial Intelligence Realized Through Machine Learning Algorithms Seems To Be The Only Viable Solution To Implement Perception, Enable Pilot Assistants And Eventually Full Autonomy To Uas. Currently, Many Uas Have Some Kind Of Conventional Computer Vision (Cv) Helping Them In Obstacle Avoidance Or Target Acquisition. Interestingly Though, Since 2012 Deep Neural Networks (Dnn) Have Dramatically Outperformed Conventional Cv Algorithms In Those Tasks And Pushed Artificial Intelligence (Ai) Limits In A Variety Of Other Applications Including, But Not Limited, To Object Recognition, Video Analytics, Decision Making And Control, Speech Recognition, Etc. Unfortunately, The Computational Power Required For Real-Time Dnn Operation Can Still Only Be Delivered By Bulky, Expensive, Slow, Heavy And Energy-Hungry Digital Systems Like Gpus. This Is Why Mentium Is Devoted To Delivering Disruptive Technology In The Field Of Machine Learning Hardware Accelerators, And In Particular For This Project, Into The Deep Learning Hardware Accelerators Field. Experimental Data And Phase I Results That Our Hardware Can Deliver 100X To 1000X Gain In Speed And In Power Efficiency Compared To Other Stateof- The-Art Accelerators. Our Final Product Will Be Able To Analyze, In Real-Time, Big Data Streams Coming From Cameras, Sensors And/Or Avionics And To Categorize (Classify) Them For The Purpose Of Decision Making Or Object Localization To Achieve Better Navigation And Collision Avoidance In Uas. The Same Hardware Processor Will Be Deployable In The Air Traffic Systems, For Real-Time Data Analysis And Decision-Making. All With More Than 10X Reduction In Cost And Power Consumption. This Distruptive Technology Is Based On An Analogcomputational Core, Exploiting The Memory Devices To Carry Out The Computation At A Physical Level. Analog Computation Is Inherently Faster And More Efficient Than The Digital One, While The In-Memory Computation Removes The Data Transfer Bottleneck.
- 22 Sep 2021
- Artificial Intelligence Realized Through Machine Learning Algorithms Seems To Be The Only Viable Solution To Implement Perception, Enable Pilot Assistants And Eventually Full Autonomy To Uas. Currently, Many Uas Have Some Kind Of Conventional Computer Vision (Cv) Helping Them In Obstacle Avoidance Or Target Acquisition. Interestingly Though, Since 2012 Deep Neural Networks (Dnn) Have Dramatically Outperformed Conventional Cv Algorithms In Those Tasks And Pushed Artificial Intelligence (Ai) Limits In A Variety Of Other Applications Including, But Not Limited, To Object Recognition, Video Analytics, Decision Making And Control, Speech Recognition, Etc. Unfortunately, The Computational Power Required For Real-Time Dnn Operation Can Still Only Be Delivered By Bulky, Expensive, Slow, Heavy And Energy-Hungry Digital Systems Like Gpus. This Is Why Mentium Is Devoted To Delivering Disruptive Technology In The Field Of Machine Learning Hardware Accelerators, And In Particular For This Project, Into The Deep Learning Hardware Accelerators Field. Experimental Data And Phase I Results That Our Hardware Can Deliver 100X To 1000X Gain In Speed And In Power Efficiency Compared To Other Stateof- The-Art Accelerators. Our Final Product Will Be Able To Analyze, In Real-Time, Big Data Streams Coming From Cameras, Sensors And/Or Avionics And To Categorize (Classify) Them For The Purpose Of Decision Making Or Object Localization To Achieve Better Navigation And Collision Avoidance In Uas. The Same Hardware Processor Will Be Deployable In The Air Traffic Systems, For Real-Time Data Analysis And Decision-Making. All With More Than 10X Reduction In Cost And Power Consumption. This Distruptive Technology Is Based On An Analogcomputational Core, Exploiting The Memory Devices To Carry Out The Computation At A Physical Level. Analog Computation Is Inherently Faster And More Efficient Than The Digital One, While The In-Memory Computation Removes The Data Transfer Bottleneck.
- Nasa Shared Services Center
- National Aeronautics And Space Administration (Nasa)
- $1,129,356.00
- National Aeronautics And Space Administration (Nasa)