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collection of works on neural network based embedded applications
embedded-neural-network's Introduction
This is a collection of papers aiming at reducing model sizes or the ASIC/FPGA accelerator for Machine Learning, especially deep neural network related applications. (Inspiled by Neural-Networks-on-Silicon )
Notes can be found in my personal blog. (TODO )
structured matrices
Structured Convolution Matrices for Energy-efficient Deep learning. (IBM Research–Almaden)
Structured Transforms for Small-Footprint Deep Learning. (Google Inc)
An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections.
Hashing
Functional Hashing for Compressing Neural Networks. (Baidu Inc)
Compressing Neural Networks with the Hashing Trick. (Washington University + NVIDIA)
Learning compact recurrent neural networks. (University of Southern California + Google)
Teacher-Student Mechanism (Distilling)
Distilling the Knowledge in a Neural Network. (Google Inc)
Sequence-Level Knowledge Distillation. (Harvard University)
Fixed-precision training and storage
Deep neural networks are robust to weight binarization and other non-linear distortions. (IBM Research–Almaden)
XNOR-Net, Ternary Weight Networks (TWNs), Binary-net and their variants.
Recurrent Neural Networks With Limited Numerical Precision. (ETH Zurich + Montréal@Yoshua Bengio)
Neural Networks with Few Multiplications. (Montréal@Yoshua Bengio)
1-Bit Stochastic Gradient Descent and its Application to Data-Parallel Distributed Training of Speech DNNs. (Tsinghua University + Microsoft)
Towards the Limit of Network Quantization. (Samsung US R&D Center)
Incremental Network Quantization_Towards Lossless CNNs with Low-precision Weights. (Intel Labs China)
Loss-aware Binarization of Deep Networks. (Hong Kong University of Science and Technology)
Trained Ternary Quantization. (Tsinghua University + Stanford University + NVIDIA)
Sparsity regularizers & Pruning
Learning both Weights and Connections for Efficient Neural Networks. (SongHan, Stanford University)
Deep Compression, EIE. (SongHan, Stanford University)
Dynamic Network Surgery for Efficient DNNs. (Intel)
Compression of Neural Machine Translation Models via Pruning. (Stanford University)
Accelerating Deep Convolutional Networks using low-precision and sparsity. (Intel)
Faster CNNs with Direct Sparse Convolutions and Guided Pruning. (Intel)
Exploring Sparsity in Recurrent Neural Networks. (Baidu Research)
Pruning Convolutional Neural Networks for Resource Efficient Inference. (NVIDIA)
Pruning Filters for Efficient ConvNets. (University of Maryland + NEC Labs America)
Soft Weight-Sharing for Neural Network Compression. (University of Amsterdam)
Sparsely-Connected Neural Networks_Towards Efficient VLSI Implementation of Deep Neural Networks. (McGill University)
Training Compressed Fully-Connected Networks with a Density-Diversity Penalty. (University of Washington)
Low-rank matrix factorization & Tensor Decomposition
Learning compact recurrent neural networks. (University of Southern California + Google)
Others coming soon!
Conditional (Adaptive) Computing
Adaptive Computation Time for Recurrent Neural Networks. (Google DeepMind@Alex Graves)
Variable Computation in Recurrent Neural Networks. (New York University + Facebook AI Research)
Spatially Adaptive Computation Time for Residual Networks. (Google, etc.)
Hierarchical Multiscale Recurrent Neural Networks. (Montréal)
Outrageously Large Neural Networks_The Sparsely-Gated Mixture-of-Experts Layer. (Google Brain + Jagiellonian University)
Benchmark and Platform Analysis
Fathom: Reference Workloads for Modern Deep Learning Methods. (Harvard University)
DeepBench: Open-Source Tool for benchmarking DL operations. (svail.github.io-Baidu)
Recurrent Neural Networks
FPGA-based Low-power Speech Recognition with Recurrent Neural Networks. (Seoul National University)
Accelerating Recurrent Neural Networks in Analytics Servers: Comparison of FPGA, CPU, GPU, and ASIC. (Intel)
ESE: Efficient Speech Recognition Engine with Compressed LSTM on FPGA. (Song Han, Stanford University, etc.)
Convolutional Neural Networks
Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks
Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC. (Intel)
FINN: A Framework for Fast, Scalable Binarized Neural Network Inference. (Xilinx Research Labs, etc.)
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