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Best Graphics Cards for Gaming in 2024
Here are the best graphics cards for gaming, from high-end to budget solutions.
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RobjHunt
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Spot on Nanna!
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Legend. Many thanks. FFBest Graphics Cards for Gaming in 2024
Here are the best graphics cards for gaming, from high-end to budget solutions.www.tomshardware.com
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Interesting job positions
euraxess.ec.europa.eu
Project
With the rise of deep learning (DL), our world braces for Artificial Intelligence (AI) in every edge device, creating an urgent need for Edge-AI processing hardware. Unlike existing solutions, this hardware needs to support high throughput, reliable, and secure AI processing at ultra-low power (ULP), combined with a very short time to market.
With its strong position in edge solutions and open processing platforms, the EU is ideally positioned to become the leader in this edge-AI market. However, certain roadblocks keep the EU from assuming this leadership role: Edge processors need to become 100x more energy efficient; Their complexity demands automated design with 10x design-time reduction; They must be secure and reliable to gain acceptance; Finally, they should be flexible and powerful to support the rapidly evolving DL domain.
CONVOLVE addresses these roadblocks in Edge-AI. To that end, it will take a holistic approach with innovations at all levels of the design stack, including:
Candidates
We are seeking two highly skilled and motivated postdoc candidates to tackle any of the following four research topics (a single candidate will focus on one topic):
Topic 1: Ultra-low power CGRA for Dynamic ANNs and SNNs: Research and develop near-memory computing engines based on Coarse-Grained Reconfigurable Architectures (CGRA) using a flexible memory fabric for Dynamic Neural Networks. These designs need to be equipped with self-healing mechanisms to (partly) recover in the event of failures, enhancing system-level reliability. The accelerators may also have knobs to exploit near-threshold and approximate computing for extreme energy-efficient operation.
Topic 2: Design-flow for SNNs and ANNs implemented in compiler: Research and develop a high-quality compiler backend for CGRAs targets supporting SNNs and ANNs. Compared to existing solutions, the energy efficiency needs to be improved by exploiting SIMD, advanced memory hierarchy, data reuse, sparsity, software operand bypassing, etc.
Topic 3: Compositional performance analysis and architecture Design Space Exploration (DSE) of a System-on-Chip (SOC): Research and develop an infrastructure to model energy & latency at the SoC level, including the SoC level memory hierarchy and processing host, as well as integrating the different accelerator component models. To support rapid evaluations needed for the DSE, analytical models need to be pursued. The development of compositional models will moreover enable run-time performance assessment of an application when the platform configuration changes (dynamic SoC reconfiguration) due to a failing platform component.
Topic 4: Composable and Secure SoC accelerator platform: Research and develop novel composable and real-time design techniques to realize an ultra-low-power and real-time Trusted Execution Environment (TEE) for an SoC platform consisting of RISC-V cores with several accelerators. Different security features that protect against physical attacks need to be integrated into the SoC platform, while maintaining ultra-low-power and real-time requirements of the applications. The platform should allow easy and secure integration of Post-Quantum Cryptography accelerators and Compute-In-Memory (CIM) based hardware accelerators.
Job requirements
For both positions we are looking for excellent, teamwork-oriented, and research-driven candidates with a PhD degree in Electrical Engineering, Computer Science or AI related topic and strong hardware/software design skills.
Postdoc positions on Seamless design of Smart Edge Processors
Project With the rise of deep learning (DL), our world braces for Artificial Intelligence (AI) in every edge device, creating an urgent need for Edge-AI processing hardware. Unlike existing solutions, this hardware needs to support high throughput, reliable, and secure AI processing at ultra-low...
OFFER DESCRIPTION
Project
With the rise of deep learning (DL), our world braces for Artificial Intelligence (AI) in every edge device, creating an urgent need for Edge-AI processing hardware. Unlike existing solutions, this hardware needs to support high throughput, reliable, and secure AI processing at ultra-low power (ULP), combined with a very short time to market.
With its strong position in edge solutions and open processing platforms, the EU is ideally positioned to become the leader in this edge-AI market. However, certain roadblocks keep the EU from assuming this leadership role: Edge processors need to become 100x more energy efficient; Their complexity demands automated design with 10x design-time reduction; They must be secure and reliable to gain acceptance; Finally, they should be flexible and powerful to support the rapidly evolving DL domain.
CONVOLVE addresses these roadblocks in Edge-AI. To that end, it will take a holistic approach with innovations at all levels of the design stack, including:
- On-edge continuous learning for improved accuracy, self-healing, and reliable adaptation to non-stationary environments
- Rethinking DL models through dynamic neural networks, event-based execution, and sparsity
- Transparent compilers supporting automated code optimizations and domain-specific languages
- Fast compositional design of System-on-Chips (SoC)
- Digital accelerators for dynamic ANN and SNN
- ULP memristive circuits for computation-in-memory
- Holistic integration in SoCs supporting secure execution with real-time guarantees
Candidates
We are seeking two highly skilled and motivated postdoc candidates to tackle any of the following four research topics (a single candidate will focus on one topic):
Topic 1: Ultra-low power CGRA for Dynamic ANNs and SNNs: Research and develop near-memory computing engines based on Coarse-Grained Reconfigurable Architectures (CGRA) using a flexible memory fabric for Dynamic Neural Networks. These designs need to be equipped with self-healing mechanisms to (partly) recover in the event of failures, enhancing system-level reliability. The accelerators may also have knobs to exploit near-threshold and approximate computing for extreme energy-efficient operation.
Topic 2: Design-flow for SNNs and ANNs implemented in compiler: Research and develop a high-quality compiler backend for CGRAs targets supporting SNNs and ANNs. Compared to existing solutions, the energy efficiency needs to be improved by exploiting SIMD, advanced memory hierarchy, data reuse, sparsity, software operand bypassing, etc.
Topic 3: Compositional performance analysis and architecture Design Space Exploration (DSE) of a System-on-Chip (SOC): Research and develop an infrastructure to model energy & latency at the SoC level, including the SoC level memory hierarchy and processing host, as well as integrating the different accelerator component models. To support rapid evaluations needed for the DSE, analytical models need to be pursued. The development of compositional models will moreover enable run-time performance assessment of an application when the platform configuration changes (dynamic SoC reconfiguration) due to a failing platform component.
Topic 4: Composable and Secure SoC accelerator platform: Research and develop novel composable and real-time design techniques to realize an ultra-low-power and real-time Trusted Execution Environment (TEE) for an SoC platform consisting of RISC-V cores with several accelerators. Different security features that protect against physical attacks need to be integrated into the SoC platform, while maintaining ultra-low-power and real-time requirements of the applications. The platform should allow easy and secure integration of Post-Quantum Cryptography accelerators and Compute-In-Memory (CIM) based hardware accelerators.
Job requirements
For both positions we are looking for excellent, teamwork-oriented, and research-driven candidates with a PhD degree in Electrical Engineering, Computer Science or AI related topic and strong hardware/software design skills.
Fact Finder
Top 20
Hope Peter and Anil don’t see this add.Interesting job positions
Postdoc positions on Seamless design of Smart Edge Processors
Project With the rise of deep learning (DL), our world braces for Artificial Intelligence (AI) in every edge device, creating an urgent need for Edge-AI processing hardware. Unlike existing solutions, this hardware needs to support high throughput, reliable, and secure AI processing at ultra-low...
OFFER DESCRIPTION
Project
With the rise of deep learning (DL), our world braces for Artificial Intelligence (AI) in every edge device, creating an urgent need for Edge-AI processing hardware. Unlike existing solutions, this hardware needs to support high throughput, reliable, and secure AI processing at ultra-low power (ULP), combined with a very short time to market.
With its strong position in edge solutions and open processing platforms, the EU is ideally positioned to become the leader in this edge-AI market. However, certain roadblocks keep the EU from assuming this leadership role: Edge processors need to become 100x more energy efficient; Their complexity demands automated design with 10x design-time reduction; They must be secure and reliable to gain acceptance; Finally, they should be flexible and powerful to support the rapidly evolving DL domain.
CONVOLVE addresses these roadblocks in Edge-AI. To that end, it will take a holistic approach with innovations at all levels of the design stack, including:
The CONVOLVE consortium includes some of Europe's strongest research groups and industries, covering the whole design stack and value chain. In a community effort, we will demonstrate Edge-AI computing in real-life vision and audio domains. By combining these innovative ULP and fast design solutions, CONVOLVE will, for the first time, enable reliable, smart, and energy-efficient edge-AI devices at a rapid time-to-market and low cost, and as such, opens the road for EU leadership in edge-processing.
- On-edge continuous learning for improved accuracy, self-healing, and reliable adaptation to non-stationary environments
- Rethinking DL models through dynamic neural networks, event-based execution, and sparsity
- Transparent compilers supporting automated code optimizations and domain-specific languages
- Fast compositional design of System-on-Chips (SoC)
- Digital accelerators for dynamic ANN and SNN
- ULP memristive circuits for computation-in-memory
- Holistic integration in SoCs supporting secure execution with real-time guarantees
Candidates
We are seeking two highly skilled and motivated postdoc candidates to tackle any of the following four research topics (a single candidate will focus on one topic):
Topic 1: Ultra-low power CGRA for Dynamic ANNs and SNNs: Research and develop near-memory computing engines based on Coarse-Grained Reconfigurable Architectures (CGRA) using a flexible memory fabric for Dynamic Neural Networks. These designs need to be equipped with self-healing mechanisms to (partly) recover in the event of failures, enhancing system-level reliability. The accelerators may also have knobs to exploit near-threshold and approximate computing for extreme energy-efficient operation.
Topic 2: Design-flow for SNNs and ANNs implemented in compiler: Research and develop a high-quality compiler backend for CGRAs targets supporting SNNs and ANNs. Compared to existing solutions, the energy efficiency needs to be improved by exploiting SIMD, advanced memory hierarchy, data reuse, sparsity, software operand bypassing, etc.
Topic 3: Compositional performance analysis and architecture Design Space Exploration (DSE) of a System-on-Chip (SOC): Research and develop an infrastructure to model energy & latency at the SoC level, including the SoC level memory hierarchy and processing host, as well as integrating the different accelerator component models. To support rapid evaluations needed for the DSE, analytical models need to be pursued. The development of compositional models will moreover enable run-time performance assessment of an application when the platform configuration changes (dynamic SoC reconfiguration) due to a failing platform component.
Topic 4: Composable and Secure SoC accelerator platform: Research and develop novel composable and real-time design techniques to realize an ultra-low-power and real-time Trusted Execution Environment (TEE) for an SoC platform consisting of RISC-V cores with several accelerators. Different security features that protect against physical attacks need to be integrated into the SoC platform, while maintaining ultra-low-power and real-time requirements of the applications. The platform should allow easy and secure integration of Post-Quantum Cryptography accelerators and Compute-In-Memory (CIM) based hardware accelerators.
Job requirements
For both positions we are looking for excellent, teamwork-oriented, and research-driven candidates with a PhD degree in Electrical Engineering, Computer Science or AI related topic and strong hardware/software design skills.
FFFact Finder
Top 20
Yes I noticed a small crease in the table cloth so I sent a message to Jerome Nadel. He said he would take down the Tweet, get it pressed and put back up. Heads will roll once they establish who allowed this to happen.
FFAKIDA BALLISTA
D
Deleted member 118
Guest
Yes I noticed a small crease in the table cloth so I sent a message to Jerome Nadel. He said he would take down the Tweet, get it pressed and put back up. Heads will roll once they establish who allowed this to happen.
AKIDA BALLISTA
White Horse
Regular
Following through, it says job has expired.Interesting job positions
Postdoc positions on Seamless design of Smart Edge Processors
Project With the rise of deep learning (DL), our world braces for Artificial Intelligence (AI) in every edge device, creating an urgent need for Edge-AI processing hardware. Unlike existing solutions, this hardware needs to support high throughput, reliable, and secure AI processing at ultra-low...
OFFER DESCRIPTION
Project
With the rise of deep learning (DL), our world braces for Artificial Intelligence (AI) in every edge device, creating an urgent need for Edge-AI processing hardware. Unlike existing solutions, this hardware needs to support high throughput, reliable, and secure AI processing at ultra-low power (ULP), combined with a very short time to market.
With its strong position in edge solutions and open processing platforms, the EU is ideally positioned to become the leader in this edge-AI market. However, certain roadblocks keep the EU from assuming this leadership role: Edge processors need to become 100x more energy efficient; Their complexity demands automated design with 10x design-time reduction; They must be secure and reliable to gain acceptance; Finally, they should be flexible and powerful to support the rapidly evolving DL domain.
CONVOLVE addresses these roadblocks in Edge-AI. To that end, it will take a holistic approach with innovations at all levels of the design stack, including:
The CONVOLVE consortium includes some of Europe's strongest research groups and industries, covering the whole design stack and value chain. In a community effort, we will demonstrate Edge-AI computing in real-life vision and audio domains. By combining these innovative ULP and fast design solutions, CONVOLVE will, for the first time, enable reliable, smart, and energy-efficient edge-AI devices at a rapid time-to-market and low cost, and as such, opens the road for EU leadership in edge-processing.
- On-edge continuous learning for improved accuracy, self-healing, and reliable adaptation to non-stationary environments
- Rethinking DL models through dynamic neural networks, event-based execution, and sparsity
- Transparent compilers supporting automated code optimizations and domain-specific languages
- Fast compositional design of System-on-Chips (SoC)
- Digital accelerators for dynamic ANN and SNN
- ULP memristive circuits for computation-in-memory
- Holistic integration in SoCs supporting secure execution with real-time guarantees
Candidates
We are seeking two highly skilled and motivated postdoc candidates to tackle any of the following four research topics (a single candidate will focus on one topic):
Topic 1: Ultra-low power CGRA for Dynamic ANNs and SNNs: Research and develop near-memory computing engines based on Coarse-Grained Reconfigurable Architectures (CGRA) using a flexible memory fabric for Dynamic Neural Networks. These designs need to be equipped with self-healing mechanisms to (partly) recover in the event of failures, enhancing system-level reliability. The accelerators may also have knobs to exploit near-threshold and approximate computing for extreme energy-efficient operation.
Topic 2: Design-flow for SNNs and ANNs implemented in compiler: Research and develop a high-quality compiler backend for CGRAs targets supporting SNNs and ANNs. Compared to existing solutions, the energy efficiency needs to be improved by exploiting SIMD, advanced memory hierarchy, data reuse, sparsity, software operand bypassing, etc.
Topic 3: Compositional performance analysis and architecture Design Space Exploration (DSE) of a System-on-Chip (SOC): Research and develop an infrastructure to model energy & latency at the SoC level, including the SoC level memory hierarchy and processing host, as well as integrating the different accelerator component models. To support rapid evaluations needed for the DSE, analytical models need to be pursued. The development of compositional models will moreover enable run-time performance assessment of an application when the platform configuration changes (dynamic SoC reconfiguration) due to a failing platform component.
Topic 4: Composable and Secure SoC accelerator platform: Research and develop novel composable and real-time design techniques to realize an ultra-low-power and real-time Trusted Execution Environment (TEE) for an SoC platform consisting of RISC-V cores with several accelerators. Different security features that protect against physical attacks need to be integrated into the SoC platform, while maintaining ultra-low-power and real-time requirements of the applications. The platform should allow easy and secure integration of Post-Quantum Cryptography accelerators and Compute-In-Memory (CIM) based hardware accelerators.
Job requirements
For both positions we are looking for excellent, teamwork-oriented, and research-driven candidates with a PhD degree in Electrical Engineering, Computer Science or AI related topic and strong hardware/software design skills.
D
Deleted member 118
Guest
Over a month old so positions probably taken
Fact Finder
Top 20
Thank goodness I was worried that after reading what the expert financial analysts had to say Peter and Anil would apply and jump ship. Pheew that was close.
FF
AKIDA BALLISTA
D
Deleted member 118
Guest
Thank goodness I was worried that after reading what the expert financial analysts had to say Peter and Anil would apply and jump ship. Pheew that was close.
FF
AKIDA BALLISTA
Patent History
Publication number: 20220147797
Type: Application
Filed: Jan 25, 2022
Publication Date: May 12, 2022
Applicant: BrainChip, Inc. (Laguna Hills, CA)
Inventors: Douglas MCLELLAND (Laguna Hills, CA), Kristofor D. CARLSON (Laguna Hills, CA), Harshil K. PATEL (Laguna Hills, CA), Anup A. VANARSE (Laguna Hills, CA), Milind JOSHI (Perth)
Application Number: 17/583,640
Is this a sign of the changing of the guard/s...I don't think so...BUT...check out the 3 Perth based "Dream Team" getting to put their inventors hats on....I'm personally really pleased for Anup and Harshil with whom I've had the pleasure to talk with in person.
This was obviously only published 5/6 days ago, so if this has already been posted, excuse me, as I can't keep up with all the brilliant articles being posted, I'm a slow reader
Good morning from Australia's Brainchip HQ.....Perth
Publication number: 20220147797
Type: Application
Filed: Jan 25, 2022
Publication Date: May 12, 2022
Applicant: BrainChip, Inc. (Laguna Hills, CA)
Inventors: Douglas MCLELLAND (Laguna Hills, CA), Kristofor D. CARLSON (Laguna Hills, CA), Harshil K. PATEL (Laguna Hills, CA), Anup A. VANARSE (Laguna Hills, CA), Milind JOSHI (Perth)
Application Number: 17/583,640
Is this a sign of the changing of the guard/s...I don't think so...BUT...check out the 3 Perth based "Dream Team" getting to put their inventors hats on....I'm personally really pleased for Anup and Harshil with whom I've had the pleasure to talk with in person.
This was obviously only published 5/6 days ago, so if this has already been posted, excuse me, as I can't keep up with all the brilliant articles being posted, I'm a slow reader
Good morning from Australia's Brainchip HQ.....Perth
Last edited:
Sirod69
bavarian girl ;-)
So nice to see brainchip, Megachips and Edge Impuls at the Embedded Vision Summit and today on our German News there was a talk about self driving with Mercedes, really so many news and connections and then the 1000 eyes, really great I wish you a good day, and now I really know we in Europa are green tomorrow morning
SebThatGermanChap
Regular
I highly doubt you making a fool of yourself is possible.
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Deleted member 118
Guest
D
Deleted member 118
Guest
Maybe it was this
Quadric and MegaChips Form Partnership to Bring IP Products to ASIC and SoC Market
Collaboration Will Leverage High-Performance Quadric GPNPU Architecture to Deliver On-Device AI Solutions BURLINGAME, Calif., and OSAKA, Japan, May 17,
www.edge-ai-vision.com
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