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Found 9 papers, 3 papers with code
N3H-Core: Neuron-designed Neural Network Accelerator via FPGA-based Heterogeneous Computing Cores
From the software perspective, we mathematically and systematically model the latency and resource utilization of the proposed heterogeneous accelerator, regarding varying system design configurations.
Learning to Affiliate: Mutual Centralized Learning for Few-shot Classification
Few-shot learning (FSL) aims to learn a classifier that can be easily adapted to accommodate new tasks not seen during training, given only a few examples.
Simple Augmentation Goes a Long Way: ADRL for DNN Quantization
This new strategy augments the neural networks in DRL with a complementary scheme to boost the performance of learning.
Cross-modal Knowledge Reasoning for Knowledge-based Visual Question Answering
Finally, we perform graph neural networks to infer the global-optimal answer by jointly considering all the concepts.
Regularized Training and Tight Certification for Randomized Smoothed Classifier with Provable Robustness
In this work, we derive a new regularized risk, in which the regularizer can adaptively encourage the accuracy and robustness of the smoothed counterpart when training the base classifier.
Sionnx: Automatic Unit Test Generator for ONNX Conformance
Finally, through this easy-to-use specification language, we are able to build a full testing specification which leverages LLVM TableGen to automatically generate unit tests for ONNX operators with much large coverage.
Software-defined Design Space Exploration for an Efficient DNN Accelerator Architecture
It models the accelerator design task as a multi-dimensional optimization problem.
Hardware-Guided Symbiotic Training for Compact, Accurate, yet Execution-Efficient LSTM
In this work, we propose a hardware-guided symbiotic training methodology for compact, accurate, yet execution-efficient inference models.
Modeling Text with Graph Convolutional Network for Cross-Modal Information Retrieval
A dual-path neural network model is proposed for couple feature learning in cross-modal information retrieval.
