From 7faeb6c7c3703688c58bba0561d589113cc0dc5c Mon Sep 17 00:00:00 2001 From: Fengbin Tu Date: Mon, 2 Apr 2018 15:13:39 +0800 Subject: [PATCH] Update README.md Add five classical papers (the DianNao family and Zhang's FPGA'15), which were published before I built this repository. --- README.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/README.md b/README.md index 14cc250..d9941b7 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,11 @@ My name is Fengbin Tu. I'm currently pursuing my Ph.D. degree with the Institute ## Table of Contents - [My Contributions](#my-contributions) - [Conference Papers](#conference-papers) + - [2014 ASPLOS](#2014-asplos) + - [2014 MICRO](#2014-micro) + - [2015 ISCA](#2015-isca) + - [2015 ASPLOS](#2015-asplos) + - [2015 FPGA](#2015-fpga) - [2015 DAC](#2015-dac) - [2016 DAC](#2016-dac) - [2016 ISSCC](#2016-isscc) @@ -55,6 +60,22 @@ I'm working on energy-efficient architecture design for deep learning. A deep co ## Conference Papers This is a collection of conference papers that interest me. The emphasis is focused on, but not limited to neural networks on silicon. Papers of significance are marked in **bold**. My comments are marked in *italic*. + +### 2014 ASPLOS +- **DianNao: A Small-Footprint High-Throughput Accelerator for Ubiquitous Machine-Learning.** (CAS, Inria) + +### 2014 MICRO +- **DaDianNao: A Machine-Learning Supercomputer.** (CAS, Inria, Inner Mongolia University) + +### 2015 ISCA +- **ShiDianNao: Shifting Vision Processing Closer to the Sensor.** (CAS, EPFL, Inria) + +### 2015 ASPLOS +- **PuDianNao: A Polyvalent Machine Learning Accelerator.** (CAS, USTC, Inria) + +### 2015 FPGA +- **Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks.** (Peking University, UCLA) + ### 2015 DAC - Reno: A Highly-Efficient Reconfigurable Neuromorphic Computing Accelerator Design. (Universtiy of Pittsburgh, Tsinghua University, San Francisco State University, Air Force Research Laboratory, University of Massachusetts.) - Scalable Effort Classifiers for Energy Efficient Machine Learning. (Purdue University, Microsoft Research)