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collection of works aiming at reducing model sizes or the ASIC/FPGA accelerator for machine learning
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 )
Tutorials:
Hardware Accelerator : Efficient Processing of Deep Neural Networks. (link )
Model Compression : Model Compression and Acceleration for Deep Neural Networks. (link )
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.
Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank.
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)
Like What You Like: Knowledge Distill via Neuron Selectivity Transfer. (TuSimple)
Fixed-precision training and storage
Binary/Ternary Neural Networks
XNOR-Net, Ternary Weight Networks (TWNs), Binary-net and their variants.
Deep neural networks are robust to weight binarization and other non-linear distortions. (IBM Research–Almaden)
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, reddit discussion )
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)
Bayesian Compression
Bayesian Sparsification of Recurrent Neural Networks
Bayesian Compression for Deep Learning
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications. (Samsung, etc)
Learning compact recurrent neural networks. (University of Southern California + Google)
Tensorizing Neural Networks. (Skolkovo Institute of Science and Technology, etc)
Ultimate tensorization_compressing convolutional and FC layers alike. (Moscow State University, etc)
Efficient and Accurate Approximations of Nonlinear Convolutional Networks. (@CVPR2015)
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation. (New York University, etc.)
Convolutional neural networks with low-rank regularization. (Princeton University, etc.)
Learning with Tensors: Why Now and How? (Tensor-Learn Workshop @ NIPS'16)
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. (github link , Google, etc.)
Hierarchical Multiscale Recurrent Neural Networks. (Montréal)
Outrageously Large Neural Networks_The Sparsely-Gated Mixture-of-Experts Layer. (Google Brain, etc.)
Adaptive Neural Networks for Fast Test-Time Prediction. (Boston University, etc)
Dynamic Deep Neural Networks_Optimizing Accuracy-Efficiency Trade-offs by Selective Execution. (University of Michigan)
Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation . (@Yoshua Bengio)
Multi-Scale Dense Convolutional Networks for Efficient Prediction. (Cornell University, etc)
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)
BENCHIP: Benchmarking Intelligence Processors.
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. (FPGA 2017, Best Paper Award)
DNPU: An 8.1TOPS/W Reconfigurable CNN-RNN Processor for GeneralPurpose Deep Neural Networks. (KAIST, ISSCC 2017)
Hardware Architecture of Bidirectional Long Short-Term Memory Neural Network for Optical Character Recognition. (University of Kaiserslautern, etc)
Efficient Hardware Mapping of Long Short-Term Memory Neural Networks for Automatic Speech Recognition. (Master Thesis@Georgios N. Evangelopoulos)
A Fast and Power Efficient Architecture to Parallelize LSTM based RNN for Cognitive Intelligence Applications. (Tsinghua University)
Hardware Accelerators for Recurrent Neural Networks on FPGA. (Purdue University, ISCAS 2017)
Accelerating Recurrent Neural Networks: A Memory Efficient Approach. (Nanjing University)
A Fast and Power Efficient Architecture to Parallelize LSTM based RNN for Cognitive Intelligence Applications.
An Energy-Efficient Reconfigurable Architecture for RNNs Using Dynamically Adaptive Approximate Computing.
DNPU: An 8.1TOPS/W reconfigurable CNN-RNN processor for general-purpose deep neural networks.
Hardware Architecture of Bidirectional Long Short-Term Memory Neural Network for Optical Character Recognition.
A Systolically Scalable Accelerator for Near-Sensor Recurrent Neural Network Inference.
Convolutional Neural Networks
Dynamic Network Surgery for Efficient DNNs. (Intel Labs China)
Memory-Efficient Backpropagation Through Time. (Google DeepMind)
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions. (Moscow State University, etc.)
Learning Structured Sparsity in Deep Neural Networks. (University of Pittsburgh)
LightRNN: Memory and Computation-Efficient Recurrent Neural Networks. (Nanjing University + Microsoft Research)
lognet: energy-efficient neural networks using logarithmic computation. (Stanford University)
extended low rank plus diagonal adaptation for deep and recurrent neural networks. (Microsoft)
fixed-point optimization of deep neural networks with adaptive step size retraining. (Seoul National University)
implementation of efficient, low power deep neural networks on next-generation intel client platforms (Demos). (Intel)
knowledge distillation for small-footprint highway networks. (TTI-Chicago, etc)
automatic node selection for deep neural networks using group lasso regularization. (Doshisha University, etc)
accelerating deep convolutional networks using low-precision and sparsity. (Intel Labs)
Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning. (MIT)
Network Sketching: Exploiting Binary Structure in Deep CNNs. (Intel Labs China + Tsinghua University)
Spatially Adaptive Computation Time for Residual Networks. (Google, etc)
A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation. (University of Pittsburgh, etc)
Deep Tensor Convolution on Multicores. (MIT)
Beyond Filters: Compact Feature Map for Portable Deep Model. (Peking University + University of Sydney)
Combined Group and Exclusive Sparsity for Deep Neural Networks. (UNIST)
Delta Networks for Optimized Recurrent Network Computation. (Institute of Neuroinformatics, etc)
MEC: Memory-efficient Convolution for Deep Neural Network. (IBM Research)
Deciding How to Decide: Dynamic Routing in Artificial Neural Networks. (California Institute of Technology)
Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning. (ETH Zurich, etc)
Analytical Guarantees on Numerical Precision of Deep Neural Networks. (University of Illinois at Urbana-Champaign)
Variational Dropout Sparsifies Deep Neural Networks. (Skoltech, etc)
Adaptive Neural Networks for Fast Test-Time Prediction. (Boston University, etc)
Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank. (The City University of New York, etc)
Channel Pruning for Accelerating Very Deep Neural Networks. (Xi’an Jiaotong University + Megvii Inc.)
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression. (Nanjing University, etc)
Learning Efficient Convolutional Networks through Network Slimming. (Intel Labs China, etc)
Performance Guaranteed Network Acceleration via High-Order Residual Quantization. (Shanghai Jiao Tong University + Peking University)
Coordinating Filters for Faster Deep Neural Networks. (University of Pittsburgh + Duke University, etc, github link )
Towards Accurate Binary Convolutional Neural Network. (DJI)
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations. (ETH Zurich)
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning. (Duke University, etc, github link )
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks. (Intel)
Bayesian Compression for Deep Learning. (University of Amsterdam, etc)
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon. (Nanyang Technological Univ)
Training Quantized Nets: A Deeper Understanding. (University of Maryland)
Structured Bayesian Pruning via Log-Normal Multiplicative Noise. (Yandex, etc)
Runtime Neural Pruning. (Tsinghua University)
The Reversible Residual Network: Backpropagation Without Storing Activations. (University of Toronto, gihub link )
Compression-aware Training of Deep Networks. (Toyota Research Institute + EPFL)
Oral
Training and Inference with Integers in Deep Neural Networks. (Tsinghua University)
Poster
Learning Sparse NNs Through L0 Regularization
Learning Intrinsic Sparse Structures within Long Short-Term Memory
Variantional Network Quantization
Alternating Multi-BIT Quantization for Recurrent Neural Networks
Mixed Precision Training
Multi-Scale Dense Networks for Resource Efficient Image Classification
Other to be added.
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