The Experts below are selected from a list of 18 Experts worldwide ranked by ideXlab platform
Steven L Waslander - One of the best experts on this subject based on the ideXlab platform.
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Controller Class for rigid body tracking on mathsf so 3
IEEE Transactions on Automatic Control, 2021Co-Authors: Adeel Akhtar, Steven L WaslanderAbstract:In this article, we propose a novel family of Lie algebra valued functions $\mathcal {F}_R$ on special orthogonal group $\mathsf {SO}(3)$ . Each function belonging to this family induces a novel Controller that stabilizes a rigid body attitude. All Controllers induced by the family $\mathcal {F}_R$ form a Controller Class $\mathcal {C}_R$ . This novel Controller Class $\mathcal {C}_R$ contains both local and almost-global asymptotically stable Controllers, and this article presents geometric stability results of the whole Controller Class $\mathcal {C}_R$ .
Luca Benini - One of the best experts on this subject based on the ideXlab platform.
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Extending the RISC-V ISA for Efficient RNN-based 5G Radio Resource Management
2020 57th ACM IEEE Design Automation Conference (DAC), 2020Co-Authors: Renzo Andri, Tomas Henriksson, Luca BeniniAbstract:Radio Resource Management in 5G mobile communication is a challenging problem for which Recurrent Neural Networks (RNN) have shown promising results. Accelerating the compute-intensive RNN inference is therefore of utmost importance. Programmable solutions are desirable for effective 5G-RRM coping with the rapidly evolving landscape of RNN variations. In this paper, we investigate RNN inference acceleration by tuning both the instruction set and micro-architecture of a micro-Controller-Class open-source RISC-V core. We couple HW extensions with software optimizations to achieve an overall improvement in throughput and energy efficiency of 15× and 10× w.r.t. the baseline core on a wide range of RNNs used in various RRM tasks.
Benini Luca - One of the best experts on this subject based on the ideXlab platform.
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Extending the RISC-V ISA for Efficient RNN-based 5G Radio Resource Management
2020Co-Authors: Andri Renzo, Henriksson Tomas, Benini LucaAbstract:Radio Resource Management (RRM) in 5G mobile communication is a challenging problem for which Recurrent Neural Networks (RNN) have shown promising results. Accelerating the compute-intensive RNN inference is therefore of utmost importance. Programmable solutions are desirable for effective 5G-RRM top cope with the rapidly evolving landscape of RNN variations. In this paper, we investigate RNN inference acceleration by tuning both the instruction set and micro-architecture of a micro-Controller-Class open-source RISC-V core. We couple HW extensions with software optimizations to achieve an overall improvement in throughput and energy efficiency of 15$\times$ and 10$\times$ w.r.t. the baseline core on a wide range of RNNs used in various RRM tasks
Adeel Akhtar - One of the best experts on this subject based on the ideXlab platform.
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Controller Class for rigid body tracking on mathsf so 3
IEEE Transactions on Automatic Control, 2021Co-Authors: Adeel Akhtar, Steven L WaslanderAbstract:In this article, we propose a novel family of Lie algebra valued functions $\mathcal {F}_R$ on special orthogonal group $\mathsf {SO}(3)$ . Each function belonging to this family induces a novel Controller that stabilizes a rigid body attitude. All Controllers induced by the family $\mathcal {F}_R$ form a Controller Class $\mathcal {C}_R$ . This novel Controller Class $\mathcal {C}_R$ contains both local and almost-global asymptotically stable Controllers, and this article presents geometric stability results of the whole Controller Class $\mathcal {C}_R$ .
Renzo Andri - One of the best experts on this subject based on the ideXlab platform.
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Extending the RISC-V ISA for Efficient RNN-based 5G Radio Resource Management
2020 57th ACM IEEE Design Automation Conference (DAC), 2020Co-Authors: Renzo Andri, Tomas Henriksson, Luca BeniniAbstract:Radio Resource Management in 5G mobile communication is a challenging problem for which Recurrent Neural Networks (RNN) have shown promising results. Accelerating the compute-intensive RNN inference is therefore of utmost importance. Programmable solutions are desirable for effective 5G-RRM coping with the rapidly evolving landscape of RNN variations. In this paper, we investigate RNN inference acceleration by tuning both the instruction set and micro-architecture of a micro-Controller-Class open-source RISC-V core. We couple HW extensions with software optimizations to achieve an overall improvement in throughput and energy efficiency of 15× and 10× w.r.t. the baseline core on a wide range of RNNs used in various RRM tasks.