hc

README.md

@@ -9,7 +9,11 @@ My name is Fengbin Tu. I'm currently pursuing my Ph.D. degree with the Institute
    - 2015: [ISCA](#2015-isca), [ASPLOS](#2015-asplos), [FPGA](#2015-fpga), [DAC](#2015-dac)
    - 2016: [ISSCC](#2016-isscc), [ISCA](#2016-isca), [MICRO](#2016-micro), [HPCA](#2016-hpca), [DAC](#2016-dac), [FPGA](#2016-fpga), [ICCAD](#2016-iccad), [DATE](#2016-date), [ASPDAC](#2016-aspdac), [VLSI](#2016-vlsi), [FPL](#2016-fpl)
    - 2017: [ISSCC](#2017-isscc), [ISCA](#2017-isca), [MICRO](#2017-micro), [HPCA](#2017-hpca), [ASPLOS](#2017-asplos), [DAC](#2017-dac), [FPGA](#2017-fpga), [ICCAD](#2017-iccad), [DATE](#2017-date), [VLSI](#2017-vlsi), [FCCM](#2017-fccm), [HotChips](#2017-hotchips)
+<<<<<<< HEAD
    - 2018: [ISSCC](#2018-isscc), [ISCA](#2018-isca), [HPCA](#2018-hpca), [ASPLOS](#2018-asplos), [DAC](#2018-dac), [FPGA](#2018-fpga), [DATE](#2018-date), [ASPDAC](#2018-aspdac), [VLSI](#2018-vlsi), [Hotchips](#2018-hotchips)
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+   - 2018: [ISSCC](#2018-isscc), [ISCA](#2018-isca), [HPCA](#2018-hpca), [ASPLOS](#2018-asplos), [DAC](#2018-dac), [FPGA](#2018-fpga), [DATE](#2018-date), [ASPDAC](#2018-aspdac), [VLSI](#2018-vlsi)
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  - [Important Topics](#important-topics)
    - [Tutorial and Survey](#tutorial-and-survey)
@@ -407,6 +411,7 @@ Deep Learning Accelerators** (New York Univ., IIT Kanpur)
 - **DrAcc: A DRAM Based Accelerator for Accurate CNN Inference.** (National Univ. of Defense Technology, Indiana Univ., Univ. of Pittsburgh)
 - **On-Chip Deep Neural Network Storage With Multi-Level eNVM.** (Harvard)
 - VRL-DRAM: Improving DRAM Performance via Variable Refresh Latency. (Drexel Univ. ETHZ)
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 ### 2018 Hotchips
 - **ARM's First Generation ML Processor.** (ARM)
@@ -416,6 +421,8 @@ Deep Learning Accelerators** (New York Univ., IIT Kanpur)
 - SMIV: A 16nm SoC with Efficient and Flexible DNN Acceleration for Intelligent IoT Devices. (ARM)
 - NVIDIA's Xavier System-on-Chip. (NVIDIA)
 - Xilinx Project Everest: HW/SW Programmable Engine. (Xilinx)
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 ## Important Topics
 This is a collection of papers on other important topics related to neural networks. Papers of significance are marked in **bold**. My comments are in marked in *italic*.