README.md

Papers Reading List.

• 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)

Table of Contents

• Network Compression
  • Parameter Sharing
  • Teacher-Student Mechanism (Distilling)
  • Fixed-precision training and storage
  • Sparsity regularizers & Pruning
  • Tensor Decomposition
  • Conditional (Adaptive) Computing
  • Compression through Bayesian Method
• Hardware Accelerator
  • Benchmark and Platform Analysis
  • Recurrent Neural Networks
• Conference Papers
  • NIPS 2016
  • ICASSP 2017
  • CVPR 2017
  • ICML 2017
  • ICCV 2017
  • NIPS 2017

Network Compression

Parameter Sharing

• 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

Tensor Decomposition

• 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)

Hardware Accelerator

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

• Please refer to Neural-Networks-on-Silicon

Conference Papers

NIPS 2016

• 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)

ICASSP 2017

• 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)

CVPR 2017

• 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)

ICML 2017

• 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)

ICCV 2017

• 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)

NIPS 2017

• 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)