/Embedded-Neural-Network

collection of works aiming at reducing model sizes or the ASIC/FPGA accelerator for machine learning

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)
  • A good tutorial here about this research area.

Table of Contents

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)

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)

Bayesian Compression

  • To be updated.

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)

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)

Convolutional Neural Networks

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)