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The second generation of the FINN framework is described, an end-to-end tool that enables design-space exploration and automates the creation of fully customized inference engines on FPGAs that optimizes for given platforms, design targets, and a specific precision. Convolutional Neural Networks have rapidly become the most successful machine-learning algorithm, enabling ubiquitous machine.
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FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks Authors: Michaela Blott Xilinx Inc. Thomas B. Preußer ETH Zurich Nicholas Fraser Giulio Gambardella.
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FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks Michaela Blott, Thomas B. Preußer, +3 authors Yaman Umuroglu Published 12 September 2018 Computer Science, Engineering arXiv: Hardware Architecture TLDR
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FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. It specifically targets quantized neural networks, with emphasis on generating dataflow-style architectures customized for each network. The resulting FPGA accelerators are highly efficient and can yield high throughput and low latency.
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FINN-R uses a quantization-aware intermediate representation to enable QNN-specific optimiza- tions and has a modular frontend/transform/backend structure for flexibility as is shown in Fig.4. The focus of this article is on the architecture of inference accelerators, their optimization and
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FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks Michaela Blott, Thomas Preusser, Nicholas Fraser, Giulio Gambardella, Kenneth O'Brien, Yaman Umuroglu
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FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. It specifically targets quantized neural networks, with emphasis on generating dataflow-style architectures customized for each network.
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FINN- R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks | Request PDF FINN- R: An End-to-End Deep-Learning Framework for Fast Exploration of.
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In this article, we describe the second generation of the FINN framework, an end-to-end tool which enables design space exploration and automates the creation of fully customized inference engines on FPGAs. Given a neural network description, the tool optimizes for given platforms, design targets and a specific precision.
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Next Article LBA35 Camrelizumab (C) plus rivoceranib (R) vs. sorafenib (S) as first-line therapy for unresectable hepatocellular carcinoma (uHCC): A randomized, phase III trial Background The global, randomized, double-blind, Ph3 LEAP-002 study (NCT03713593) evaluated the efficacy and safety of lenvatinib + pembrolizumab (lenva + pembro) vs.
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In this article, we describe the second generation of the FINN framework, an end-to-end tool which enables design space exploration and automates the creation of fully customized inference engines on FPGAs. Given a neural network description, the tool optimizes for given platforms, design targets and a specific precision. We introduce.
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16 FINN-R:AnEnd-to-EndDeep-LearningFrameworkforFast ExplorationofQuantizedNeuralNetworks MICHAELABLOTT,THOMASB.PREUßER,NICHOLASJ.FRASER,GIULIO GAMBARDELLA,KENNETHO.
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Convolutional Neural Networks have rapidly become the most successful machine-learning algorithm, enabling ubiquitous machine vision and intelligent decisions on even embedded computing systems. While the underlying arithmetic is structurally simple,.
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