逢迎白雪
发表于 2025-3-25 04:49:22
Conference proceedings 2020papers are organized in topical sections named: Best Paper Session; AI Challenges on Cambircon using AIBenc; AI Challenges on RISC-V using AIBench; AI Challenges on X86 using AIBench; AI Challenges on 3D Face Recognition using AIBench; Benchmark; AI and Edge; Big Data; Datacenter; Performance Analysis; Scientific Computing..
mastoid-bone
发表于 2025-3-25 08:18:01
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意外
发表于 2025-3-25 15:19:55
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强制令
发表于 2025-3-25 18:19:19
Infectious Diseases and Arthropodspose RVTensor that a light-weight neural network inference framework based on the RISC-V architecture. RVTensor is based on the SERVE.r platform and is optimized for resource-poor scenarios. Our experiments demonstrate that the accuracy of RVTensor and the Keras is the same.
江湖郎中
发表于 2025-3-25 20:01:08
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peak-flow
发表于 2025-3-26 00:49:40
Infectious Diseases and Arthropodsamming models, CUDA and OpenACC. We present the influence of the programming model on the performance and scaling characteristics. We also leverage the insights of the Roofline Scaling Trajectory analysis to tune some of the NAS Parallel Benchmarks, achieving up to 2. speedup.
CHAFE
发表于 2025-3-26 06:37:27
Infectious Diseases and Nanomedicine I to enabling deep learning inference on RISC-V. Experimental results show that in our work, there is a great gap between the performance of deep learning inference on RISC-V and that on x86; thus compared with direct compilation on RISC-V, cross-compilation on x86 is a better option to significantly improve development efficiency.
积云
发表于 2025-3-26 11:03:57
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Aviary
发表于 2025-3-26 16:43:30
Performance Analysis of GPU Programming Models Using the Roofline Scaling Trajectoriesamming models, CUDA and OpenACC. We present the influence of the programming model on the performance and scaling characteristics. We also leverage the insights of the Roofline Scaling Trajectory analysis to tune some of the NAS Parallel Benchmarks, achieving up to 2. speedup.
Obstacle
发表于 2025-3-26 19:39:55
AIRV: Enabling Deep Learning Inference on RISC-V to enabling deep learning inference on RISC-V. Experimental results show that in our work, there is a great gap between the performance of deep learning inference on RISC-V and that on x86; thus compared with direct compilation on RISC-V, cross-compilation on x86 is a better option to significantly improve development efficiency.