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Section: Partnerships and Cooperations
International Initiatives
Inria International Labs
EPFL-Inria
Associate Team involved in the International Lab:
IoTA
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Title: Ultra-Low Power Computing Platform for IoT leveraging Controlled Approximation
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International Partner (Institution - Laboratory - Researcher):
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See also: https://team.inria.fr/cairn/IOTA
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Energy issues are central to the evolution of the Internet of Things (IoT), and more generally to the ICT industry. Current low-power design techniques cannot support the estimated growth in number of IoT objects and at the same time keep the energy consumption within sustainable bounds, both on the IoT node side and on cloud/edge-cloud side. This project aims to build on the preliminary results on inexact and exact sub/near-threshold circuit design to achieve major energy consumption reductions by enabling adaptive accuracy control of applications. Advanced ultra low-power hardware design methods utilize very low supply voltage, such as in near-threshold and sub-threshold designs. These emerging technologies are very promising avenues to decrease active and stand-by-power in electronic devices. To move another step forward, recently, approximate computing has become a major field of research in the past few years. IoTA proposes to address, in a consistent fashion, the entire design stack, from hardware design, up to software application analysis, compiler optimizations, and dynamic energy management. We do believe that combining sub-near-threshold with inexact circuits on the hardware side and, in addition, extending this with intelligent and adaptive power management on the software side will produce outstanding results in terms of energy reduction, i.e., at least one order of magnitude, in IoT. The main scientific challenge is twofold: (1) to add adaptive accuracy to hardware blocks built in near/sub threshold technology and (2) to provide the tools and methods to program and make efficient use of these hardware blocks for applications in the IoT domain. This entails developing approximate computing units, on one side, and methods and tools, on the other side, to rigorously explore trade-offs between accuracy and energy consumption in IoT systems. The expertise of the members of the two teams is complementary and covers all required technical knowledge necessary to reach our objectives, i.e., ultra low power hardware design (EPFL), approximate operators and functions (Inria, EPFL), formal analysis of precision in algorithms (Inria), and static and dynamic energy management (Inria, EPFL). Finally, the proof of concept will consist of results on (1) an adaptive, inexact or exact, ultra-low power microprocessor in 28 nm process and (2) a real prototype implemented in an FPGA platform combining processors and hardware accelerators. Several software use-cases relevant for the IoT domain will be considered, e.g., embedded vision, IoT sensors data fusion, to practically demonstrate the benefits of our approach.
Inria Associate Teams Not Involved in an Inria International Labs
IntelliVIS
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Title: Design Automation for Intelligent Vision Hardware in Cyber Physical Systems
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International Partner (Institution - Laboratory - Researcher):
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The proposed collaborative research work is focused on the design and development of artificial intelligence based embedded vision architectures for cyber physical systems (CPS). Embedded vision architectures for cyber physical systems (CPS), sometimes referred to as “Visual IoT”, are challenging to design because of primary constraints of compute resources, energy and power management. Embedded vision nodes in CPS, when designed with the application of Artificial Intelligence principles and algorithms, will turn into intelligent nodes (self-learning devices) capable of performing computation and inference at the node resulting in node-level cognition. This would allow only necessary and relevant post processed data to be sent to a human or a computer-based analyst for further processing and refinement in results. However, design and development of such nodes is non-trivial. Many existing computer vision algorithms, typically ported to embedded platforms, are compute and memory intensive thus limiting the operational time when ported to battery powered devices. In addition, transmission of captured visual data, with minimal processing at the node to extract actionable insights poses increased demands on computational, communication and energy requirements. Visual saliency i.e. extraction of key features or regions of interest in images or videos captured by an embedded vision node and related post processing for inference using AI techniques is an interesting and challenging research direction. The primary reason being that such an approach is expected to cover a wider range of application specific scenarios than statically determined approaches specific to each scenario involving remote off-loading of compute or scenario specific data on servers. Apart from a general approach to visual saliency in nodes using AI based methods (machine and deep learning methods), another principal goal of the proposed project is also to examine and propose methods that allow rapid deployment of AI techniques in these nodes. Many AI techniques are data driven and for a node to adapt from one environment or application specific scenario to another, rapid deployment of AI techniques over the air (OTA) would be an interesting and challenging research direction.
Inria International Partners
DARE
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Title: Design space exploration Approaches for Reliable Embedded systems
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International Partner (Institution - Laboratory - Researcher):
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This collaborative research focuses on methodologies to design low cost and efficient techniques for safety-critical embedded systems, which require high performance and safety implying both fault tolerance and hard real-time constraints. More precisely, the objective is to develop Design Space Exploration (DSE) methodology applicable to any platform domain to drive the design of adaptive predictable low cost and efficient error detection techniques. Run-time dynamic control mechanisms are proposed to actively optimize system fault tolerance by exploring the trade-offs between predictability, reliability, performance and energy consumption using the information received from the environment and the platform during execution. In contrast to design-time static approaches the dynamism can then be exploited to improve energy consumption and performance.
LRS
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Title: Loop unRolling Stones: compiling in the polyhedral model
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International Partner (Institution - Laboratory - Researcher):
HARAMCOP
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Title: Hardware accelerators modeling using constraint-based programming
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International Partner (Institution - Laboratory - Researcher):
DeLeES
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Title: Energy-efficient Deep Learning Systems for Low-cost Embedded Systems
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International Partner (Institution - Laboratory - Researcher):
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This collaboration is centered around creation of deep-learning inference systems which are energy efficient and low cost. There are two design approaches: (i) an all-digital low-precision system, and (ii) mixed analog/digital low-precision system.
Informal International Partners
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Dept. of Electrical and Computer Engineering, Concordia University (Canada), Optical network-on-chip, manycore architectures.
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LSSI laboratory, Québec University in Trois-Rivières (Canada), Design of architectures for digital filters and mobile communications.
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Department of Electrical and Computer Engineering, University of Patras (Greece), Wireless Sensor Networks, Worst-Case Execution Time, Priority Scheduling.