LP
Lucian Petrica
Authored
3 records found
Elastic-DF
Scaling Performance of DNN Inference in FPGA Clouds through Automatic Partitioning
Customized compute acceleration in the datacenter is key to the wider roll-out of applications based on deep neural network (DNN) inference. In this article, we investigate how to maximize the performance and scalability of field-programmable gate array (FPGA)-based pipeline data
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Convolutional Neural Network (CNN) dataflow inference accelerators implemented in Field-Programmable Gate Arrays (FPGAs) have demonstrated increased energy efficiency and lower latency compared to CNN execution on CPUs or GPUs. However, the complex shapes of CNN parameter memorie
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Convolutional Neural Network (CNN) dataflow inference accelerators implemented in Field-Programmable Gate Arrays (FPGAs) have demonstrated increased energy efficiency and lower latency compared to CNN execution on CPUs or GPUs. However, the complex shapes of CNN parameter memorie
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Contributed
1 records found
Optimizing Memory Mapping for Dataflow Inference Accelerators
Efficient Memory Utilization on FPGAs
Convolutional Neural Network (CNN) inference has gained a significant amount of traction for performing tasks like speech recognition and image classification. To improve the accuracy with which these tasks can be performed, CNNs are typically designed to be deep, encompassing a
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