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About This Project

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.

ISCA 2015

Title Authors Area Organization
1 ShiDianNao: Shifting Vision Processing Closer to the Sensor Zidong Du China ICT

ISCA 2016

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

ISCA 2017

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

ISCA 2018

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

ISCA 2019

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

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