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Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile.

, , , , и . CoRR, (2022)

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NLCMAP: A Framework for the Efficient Mapping of Non-Linear Convolutional Neural Networks on FPGA Accelerators., , , , , , и . ICIP, стр. 926-930. IEEE, (2022)Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations., , , , , и . ISLPED, стр. 27:1-27:6. ACM, (2022)Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile., , , , и . CoRR, (2022)NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks., , , , , и . ICCAD, стр. 114:1-114:9. IEEE, (2020)FasTrCaps: An Integrated Framework for Fast yet Accurate Training of Capsule Networks., , , , , , и . IJCNN, стр. 1-8. IEEE, (2020)X-TrainCaps: Accelerated Training of Capsule Nets through Lightweight Software Optimizations., , , , , , и . CoRR, (2019)11.3 Metis AIPU: A 12nm 15TOPS/W 209.6TOPS SoC for Cost- and Energy-Efficient Inference at the Edge., , , , , , , , , и 38 other автор(ы). ISSCC, стр. 212-214. IEEE, (2024)Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tiles., , , , и . MICRO, стр. 582-598. IEEE, (2022)RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks., , , , , и . CoRR, (2022)An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks., , , , , и . Future Internet, 12 (7): 113 (2020)