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!
Some Statistics of the Papers
1. The yearly paper count (now only based on ISCA 2015-2019 statistics)
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.
2. The countries and regions that contribute (now only based on ISCA 2015-2019 statistics)
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.
3. Top researchers and their information (now only based on ISCA 2015-2019 statistics)
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
The Chronological Listing of Papers
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
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
Google
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