GNAW
发表于 2025-3-26 22:06:18
Achieving Efficient Realization of Kalman Filter on CGRA Through Algorithm-Architecture Co-designrovement in terms of Gflops/watt over several academically and commercially available realizations of KF is attained. In REDEFINE, we show that our implementation is scalable and the performance attained is commensurate with the underlying hardware resources.
爱得痛了
发表于 2025-3-27 02:45:26
https://doi.org/10.1007/978-3-642-22042-5ototype boards consisting of Kintex Ultrascale FPGA, and evaluation results show that the parallel execution with 20 boards achieved 4.6 times better performance than the state of art implementation on a single Virtex 7 FPGA board.
使激动
发表于 2025-3-27 08:56:49
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顾客
发表于 2025-3-27 09:47:59
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丰富
发表于 2025-3-27 15:42:48
Streamfunction and Complex Potentiale of the SqueezeNet DCNN architecture, which is designed specifically for use in embedded systems. Results show that SqueezeJet can achieve 15.16 times speed-up compared to the software implementation of SqueezeNet running on an embedded mobile processor with less than 1% drop in top-5 accuracy.
monopoly
发表于 2025-3-27 19:58:54
Introduction to Computational Techniques,ons. While the main focus of this work is flexibility, we are able to show maximum throughput for connections between two FPGAs and up to 88% saturation of the available bandwidth for connections between the FPGA and the host system.
META
发表于 2025-3-28 02:00:41
Environmental Modelling and Predictionelerator memory. The results show that while the accelerator is scalable and achieves speedups for all benchmark proofs, performance improvements are currently limited by the overhead of transitioning the proof into the accelerator memory.
vertebrate
发表于 2025-3-28 05:12:42
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文件夹
发表于 2025-3-28 07:14:40
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同音
发表于 2025-3-28 14:12:00
An FPGA/HMC-Based Accelerator for Resolution Proof Checkingelerator memory. The results show that while the accelerator is scalable and achieves speedups for all benchmark proofs, performance improvements are currently limited by the overhead of transitioning the proof into the accelerator memory.