This project aims to collect and summarize the AI-related papers for readers who are interested in AI research in academia. We plan to collect all the AI-related papers in the top-tier architecture conferences such as ISCA, MICRO and HPCA in recent years. Now, we have collected them in ISCA from 2015 to 2019 with some basic analysis. These papers will be listed below and you can find our brief summaries in "/Summarys/#year_of_the_paper/". We are glad to have your suggestions of anything about this project!
The trend of AI is generaly increasing. But now it slightly slow down in 2019. And we can find out that year 2018 takes almost half of the counts, implicating the hottest year of AI accelerators.
America is definitely the origin area of most papers. China and North Korea are still two chasing character in AI research though they have done somg terrific ahievements.
Here are the names appear most frequently on the collected papers. We collect thier public information and list below to help you find the leader researchers in this area.
| Rank | Author | Counts of paper | Region | Lab or Corp. |
|---|---|---|---|---|
| 1 | Hadi Esmaeilzadeh | 4 | US | Alternative Computing Technologies (ACT) Laboratory, University of California |
| 2 | Mingcong Song | 3 | US | Intelligent Design of Efficient Architectures Laboratory (IDEAL), University of Florida |
| 2 | Reetuparna Das | 3 | US | EECS department, University of Michigan |
| 2 | Tao Li | 3 | US | Intelligent Design of Efficient Architectures Laboratory (IDEAL), University of Florida |
| 2 | Tianshi Chen | 3 | China | Cambricon Technologies Corporation Limited(寒武纪科技) |
| 2 | Yunji Chen | 3 | China | Institute of Computing Technology, Chinese Academy of Sciences |
| 2 | Zidong Du | 3 | China | Institute of Computing Technology, Chinese Academy of Sciences |
Now we list all the papers we have collected. If it is linkable, it is linked to the summary of the paper and the summaries are still updating.
| Title | Authors | Area | Organization | |
|---|---|---|---|---|
| 1 | ShiDianNao: Shifting Vision Processing Closer to the Sensor | Zidong Du | China | ICT |
| Title | Authors | Area | Organization | |
|---|---|---|---|---|
| 1 | Cnvlutin: Ineffectual-Neuron-Free Deep Neural Network Computing | Jorge Albericio, Tayler Hetheringto | Canada | University of Toronto, University of British Columbia |
| 2 | ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars | Ali Shafiee, Vivek Srikumar | US | University of Utah，Hewlett Packard Labs |
| 3 | PRIME: A Novel Processing-in-Memory Architecture for Neural Network Computation in ReRAM-Based Main Memory | Ping Chi, Yuan Xie | US | University of California |
| 4 | EIE: Efficient Inference Engine on Compressed Deep Neural Network | Song Han, William J. Dally | US | Stanford University, NVIDIA |
| 5 | RedEye: Analog ConvNet Image Sensor Architecture for Continuous Mobile | Robert LiKamWa, Lin Zhong | US | Rice University |
| 6 | Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators | Brandon Reagen, David Brooks | US | Harvard University |
| 7 | Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks | Yu-Hsin Chen, Vivienne Sze | US | MIT, NVIDIA |
| 8 | Neurocube: A Programmable Digital Neuromorphic Architecture with High-Density 3D Memory | Duckhwan Kim, Saibal Mukhopadhyay | US | Georgia Institute of Technology |
| 9 | Cambricon: An Instruction Set Architecture for Neural Networks | Shaoli Liu, Tianshi Chen | China | CAS, Cambricon Ltd. |
| 10 | Energy Efficient Architecture for Graph Analytics Accelerators | Muhammet Mustafa Ozdal, Ozcan Ozturk | Turkey | Bilkent University |
| 11 | Accelerating Markov Random Field Inference Using Molecular Optical Gibbs Sampling Units | Siyang Wang, Alvin R. Lieberk | US | Duke University |
| Title | Authors | Area | Organization | |
|---|---|---|---|---|
| 1 | In-Datacenter Performance Analysis of a Tensor Processing Unit | Norman P. Jouppi | US | |
| 2 | Maximizing CNN Accelerator Efficiency Through Resource Partitioning | Yongming Shen | US | Stony Brook University |
| 3 | SCALEDEEP: A Scalable Compute Architecture for Learning and Evaluating Deep Networks | Swagath Venkataramani, Anand Raghunathan | US | Purdue University, Parallel Computing Lab, Intel Corporation |
| 4 | Scalpel: Customizing DNN Pruning to the Underlying Hardware Parallelism | Jiecao Yu, Scott Mahlke | US | University of Michigan, ARM |
| 5 | SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks | Angshuman Parashar, William J. Dally | US | NVIDIA, MIT, UC-Berkeley, Stanford University |
| 6 | Stream-Dataflow Acceleration | Tony Nowatzki | US | University of California, University of Wisconsin |
| 7 | Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent | Christopher De Sa, Kunle Olukotun | US | Stanford University |
| Title | Authors | Area | Organization | |
|---|---|---|---|---|
| 1 | A Configurable Cloud-Scale DNN Processor for Real-Time AI | Jeremy Fowers, Doug Burger | US | Microsoft |
| 2 | PROMISE: An End-to-End Design of a Programmable Mixed-Signal Accelerator for Machine- Learning Algorithms | Prakalp Srivastava, Mingu Kang | US | University of Illinois at Urbana-Champaign, IBM |
| 3 | Computation Reuse in DNNs by Exploiting Input Similarity | Marc Riera, Antonio Gonza ?lez | Spain | Universitat Polite ?cnica de Catalunya |
| 4 | GenAx: A Genome Sequencing Accelerator | Daichi Fujiki, Satish Narayanasamy | US | University of Michigan |
| 5 | Flexon: A Flexible Digital Neuron for Efficient Spiking Neural Network Simulations | Dayeol Lee, Jangwoo Kim | North Korea,US | Seoul National University, University of California |
| 6 | Space-Time Algebra: A Model for Neocortical Computation | James E. Smith | US | University of Wisconsin-Madison |
| 7 | Architecting a Stochastic Computing Unit with Molecular Optical Devices | Xiangyu Zhang, Alvin R. Lebeck | US | Duke University, Parabon Labs |
| 8 | RANA: Towards Efficient Neural Acceleration with Refresh-Optimized Embedded DRAM | Fengbin Tu, Shaojun Wei | China | Tsinghua University |
| 9 | Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks | Charles Eckert, Reetuparna Das | US | University of Michigan, Intel Corporation |
| 10 | RoboX: An End-to-End Solution to Accelerate Autonomous Control in Robotics | Jacob Sacks, Hadi Esmaeilzadeh | US | Georgia Institute of Technology, University of California, San Diego |
| 11 | EVA2: Exploiting Temporal Redundancy in Live Computer Vision | Mark Buckler, Adrian Sampson | US | Cornell University |
| 12 | Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision | Yuhao Zhu, Paul Whatmough | US | University of Rochetster, ARM Research |
| 13 | GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks | Amir Yazdanbakhsh, Hadi Esmaeilzadeh | US | Georgia Institute of Technology, UC San Diego, Qualcomm Technologies, Inc. |
| 14 | SnaPEA: Predictive Early Activation for Reducing Computation in Deep Convolutional Neural Networks | Vahideh Akhlaghi, Hadi Esmaeilzadeh | US | Georgia Institute of Technology, UC San Diego, Qualcomm Technologies, Inc. |
| 15 | UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition | Kartik Hegde, Christopher W. Fletche | US | University of Illinois at Urbana-Champaign, NVIDIA |
| 16 | Energy-Efficient Neural Network Accelerator Based on Outlier-Aware Low-Precision Computation | Eunhyeok Park, Sungjoo Yoo | North Korea | Seoul National University |
| 17 | Prediction Based Execution on Deep Neural Networks | Mingcong Song, Tao Li | US | University of Flirida |
| 18 | Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network | Hardik Sharma, Hadi Esmaeilzadeh | US | Georgia Institute of Technology, University of California |
| 19 | Gist: Efficient Data Encoding for Deep Neural Network Training | Animesh Jain, Gennady Pekhimenko | US,Canada | Microsoft Research, University of Toronto, Univerity of Michigan |
| 20 | The Dark Side of DNN Pruning | Reza Yazdani, Antonio Gonza ?lez | Spain | Universitat Polite ?cnica de Catalunya |
| Title | Authors | Area | Organization | |
|---|---|---|---|---|
| 1 | 3D-based Video Recognition Acceleration by Leveraging Temporal Locality | Huixiang Chen, Tao Li | US | University of Florida |
| 2 | A Stochastic-Computing based Deep Learning Framework using Adiabatic Quantum-Flux-Parametron Superconducting Technology | Ruizhe Cai, Ao Ren, Nobuyuki Yoshikawa, Yanzhi Wang | US | Northeastern University |
| 3 | Accelerating Distributed Reinforcement Learning with In-Switch Computing | Youjie Li, Jian Huang | US | UIUC |
| 4 | Eager Pruning: Algorithm and Architecture Support for Fast Training of Deep Neural Networks | Jiaqi Zhang, Tao Li | US | University of Florida |
| 5 | Laconic Deep Learning Inference Acceleration | Sayeh Sharify, Andreas Moshovos | Canada | University of Toronto |
| 6 | MnnFast: A Fast and Scalable System Architecture for Memory-Augmented Neural Networks | Hanhwi Jang, Jangwoo Kim | North Korea | POSTECH, Seoul National University |
| 7 | Sparse ReRAM Engine: Joint Exploration of Activation and Weight Sparsity in Compressed Neural Networks | Tzu-Hsien Yang | China Twain | National Taiwan University, Academia Sinica, Macronix International Co., Ltd. |
| 8 | TIE: Energy-efficient Tensor Train-based Inference Engine for Deep Neural Network | Chunhua Deng, Bo Yuan | US | Rutgers University |
| 9 | FloatPIM_ in-memory acceleration of deep neural network training with high precision | Mohsen Imani, Tajana Rosing | US | UC San Diego |
| 10 | Cambricon-F_ machine learning computers with fractal von neumann architecture | Yongwei Zhao, Yunji Chen | China | ICT, Cambricon |
| 11 | Master of none acceleration_ a comparison of accelerator architectures for analytical query processing | Andrea Lottarini, Martha A. Kim | US | Google, Columbia University |