PhD in AI Hardware Fault Tolerance

About the PhD Researcher PositionThe Inria Rennes - Bretagne Atlantique Centre is seeking a highly motivated PhD researcher to work on the impact of hardware faults on AI decisions and algorithms developed to explain AI models.The PhD researcher will be responsible for analyzing the possible failure mechanisms affecting the hardware, deriving the...

About the Research OpportunityWe are seeking a highly motivated PhD student to join our research team at Inria Rennes - Bretagne Atlantique Centre. The goal of this PhD thesis is to investigate the impact of hardware faults on AI decisions and algorithms developed to explain AI models.The successful candidate will analyze the possible failure mechanisms...

About the PhD PositionThe Inria Rennes - Bretagne Atlantique Centre is seeking a highly motivated PhD researcher to work on the impact of hardware faults on AI decisions and algorithms developed to explain AI models.The goal of the Ph.D. thesis is to study the reliability of AI hardware in the presence of faults and to design low-cost fault tolerance...

Context and BackgroundThe increasing need to distribute Artificial Intelligence (AI) applications from the cloud to edge devices has led to a growing interest in designing custom and embedded AI Hardware architectures (AI-HW). However, AI-HW is not immune to faults, which can impact AI decisions and explainability. This PhD thesis aims to study the impact of...

Research Position in Fault-Tolerant Deep Neural NetworksAs part of our research team at INRIA, we are seeking a highly motivated PhD student to work on the design and development of fault-tolerant deep neural networks. The goal of this project is to investigate the impact of hardware faults on the reliability of deep neural networks and to develop novel...

PhD Research Project: Trustworthy AI Hardware ArchitecturesWe are looking for a talented PhD student to join our research team at Inria Rennes - Bretagne Atlantique Centre. The PhD research project aims to investigate the impact of hardware faults on AI decisions and algorithms developed to explain AI models.The successful candidate will analyze the possible...

Context and benefits of the positionThe successful candidate will be part of a research team focused on designing and developing reliable hardware accelerators for deep neural networks. The goal of this PhD project is to investigate the impact of hardware faults on the accuracy of DNNs and to propose novel fault-tolerant architectures.Key...

Context and ObjectivesThe goal of this PhD thesis is to design and develop an optimized algorithm-level fault injection framework to assess the resilience of DNN hardware accelerators to hardware faults. This framework will enable the application of low-cost selective fault-tolerance strategies.Main ActivitiesThe PhD student will design and develop a...

Job Context:The INRIA research team is seeking a highly motivated PhD student to work on the development of trustworthy AI hardware architectures. The goal of the project is to design and implement AI hardware accelerators that are resilient to hardware faults and ensure the transparency of AI decisions.Key Responsibilities:Analyze the impact of hardware...

Context and BackgroundThe increasing need to distribute Artificial Intelligence (AI) applications from the cloud to edge devices has led to a growing trend of designing custom and embedded AI Hardware architectures (AI-HW) to support energy-intensive data movement, speed of computation, and large memory resources that AI requires to achieve its full...

About the Research ProjectThe Inria Rennes - Bretagne Atlantique Centre is conducting research on the impact of hardware faults on AI decisions and algorithms developed to explain AI models.The goal of the research project is to study the reliability of AI hardware in the presence of faults and to design low-cost fault tolerance approaches to efficiently...

Job DescriptionINRIA is seeking a highly motivated PhD student to work on the design and development of an optimized algorithm-level fault injection framework for assessing the resiliency of Deep Neural Network (DNN) hardware accelerators to hardware faults. The goal of this project is to enable the application of low-cost selective fault-tolerance...

Context and Background:As the field of Artificial Intelligence (AI) continues to advance, the need to distribute AI applications from the cloud to edge devices has become increasingly important. This trend aims to address issues related to data privacy, bandwidth limitations, power consumption reduction, and low latency requirements, particularly in...

Context and BackgroundAs the demand for Artificial Intelligence (AI) applications grows, there is an increasing need to distribute AI applications from the cloud to edge devices. This trend aims to address issues related to data privacy, bandwidth limitations, power consumption reduction, and low latency requirements, especially for real-time, mission- and...

Context and BackgroundThe increasing need to distribute Artificial Intelligence (AI) applications from the cloud to edge devices has led to a growing trend of designing custom and embedded AI Hardware architectures (AI-HW) to support energy-intensive data movement, speed of computation, and large memory resources that AI requires to achieve its full...

Context and BackgroundDeep Neural Networks (DNNs) are currently one of the most intensively and widely used predictive models in the field of machine learning. DNNs have proven to give very good results for many complex tasks and applications, such as object recognition in images/videos, natural language processing, satellite image recognition, robotics,...

Job Context and RequirementsWe are seeking a highly motivated PhD researcher to join our team at INRIA and contribute to the development of fault-tolerant deep learning hardware. The successful candidate will have the opportunity to work on a cutting-edge research project that aims to design and develop an optimized algorithm-level fault injection framework...

PhD Position in Unconventional AI Accelerator Reliability EnhancementAt INRIA, we are seeking a highly motivated PhD student to join our team and contribute to the development of innovative AI acceleration techniques.About the ProjectThe goal of this PhD project is to investigate the reliability of unconventional AI accelerators, specifically those based on...

Ph.D. Thesis: Reliable Deep Neural Network Hardware AcceleratorsThe aim of this Ph.D. research is to design and develop an optimized algorithm-level fault injection framework to assess the resiliency of DNN HW accelerators to HW faults.The Ph.D. student will design and develop a methodology to perform large-scale fault analysis on state-of-the-art DNN...

Job Description:We are seeking a highly motivated PhD student to join our team at INRIA Rennes - Bretagne Atlantique Centre. The successful candidate will work on the reliability enhancement of unconventional AI accelerators, focusing on the identification of hardware and software vulnerabilities in PIM accelerators for DNNs and proposing fault mitigation...