賀！本團隊鄭桂忠老師榮獲國立清華大學109年傑出產學研究獎！

Kiss Science--科學開門，青春不悶
活動地點：國立清華大學人工智慧研發中心 新竹市東區光復路二段101號
活動型態：定時導覽、實品展示、影片賞析
適合對象：高中以上籌辦單位：國立清華大學嵌入式仿神經人工智慧晶片團隊(ENIAC)
團體報名：10人以上團體報名請逕洽工作小組
現場報名：不開放現場報名

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賀！本團隊鄭桂忠老師和張孟凡老師榮獲國立清華大學108年產學合作績優教師！

本團隊於清華大學舉辦技術論壇演講活動，活動詳情如下﹕
活動名稱﹕智慧無人機之關鍵技術與應用(免費參加)
時間﹕109年8月3日 早上8:30~12:00
地點﹕清華大學台達館217教室

本團隊論文發表成功，被收錄於 IEEE Journal of Solid State Circuits
題目是："A 4Kb 1-to-8 bit Configurable 6T SRAM based Computing-in-Memory Unit-Macro for CNN-based AI Edge Processors"

本團隊論文發表成功，被收錄於 IEEE Journal of Exploratory Solid-State Computational Devices and Circuits
題目是："A Relaxed Quantization Training Method for Hardware Limitations of Resistive Random-Access Memory (ReRAM)-based Computing-In-Memory "

Abstract:Nonvolatile computing-in-memory (nvCIM) exhibits high potential for neuromorphic computing involving massive parallel computations and for achieving high energy efficiency. nvCIM is especially suitable for deep neural networks, which are required to perform large amounts of matrix–vector multiplications. However, a comprehensive quantization algorithm has yet to be developed that overcomes the hardware limitations of ReRAM-based nvCIM, such as the number of I/O, word lines (WL), and ADC outputs. In this paper, we propose a quantization training method for compressing deep models. The method comprises three steps: input and weight quantization, ReRAM convolution (ReConv), and ADC quantization. ADC quantization optimizes the error sampling problem by using the Gumbel-softmax trick. Under a 4-bit ADC of nvCIM, the accuracy only decreases by 0.05% and 1.31% for the MNIST and CIFAR-10 respectively, compared with the corresponding accuracies obtained under an ideal ADC. The experimental results indicate that the proposed method is effective for compensating the hardware limitations of nvCIM macros.

本團隊鄭桂忠教授受邀在 ISSCC Forum 2："ML at the extreme edge: Machine Learning as the Killer app"
題目是："In-Sensor and In-memory computing for Constrained Hardware for TinyML IoT Applications "

本團隊研究成果被廣泛報道（外文報道）

AFP News(French)[Link]

AP News(English)[Link]

Boerse(German)[Link]

ansa it economia(Italian)[Link]

Yahoo News(French)[Link]

本團隊研究成果被廣泛報道（中文報道）

本團隊研究成果 Demo 影片

本團隊參加未來科技展剪影！

賀！本團隊受邀到IEDM 演講
題目：AI Edge Devices Using Computing-In-Memory and Processing-In-Sensor: From System to Device

賀！本團隊入圍參加未來科技展！

English Version

賀！本團隊受邀到ASSCC 演講
題目：Are Analog and Mixed Mode Circuits the future solution of AI SoCs?” (Position: Spiking Neural Network)

賀！本團隊的論文被ASSCC接受，並成為ASSCC highlight paper
題目：A 0.5V Real-time Computational CMOS Image Sensor with Programmable Kernel for Always-on Feature Extraction
2019 IEEE Asian Solid-State Circuits Conference (ASSCC), Macao, China.

賀！本團隊和Qualcomm開展技術合作

本實驗室參與的研究成果被廣泛報道

本研究團隊受邀參加翱翔農業————無人機於智慧農業應用研討會

賀！本團隊受VLSI Symposia 邀請演講
題目："Considerations of Integrating Computing-In-Memory and Processing-In-Sensor into Convolutional Neural Network Accelerators for Low-Power Edge Devices"
@2019 Symposia on VLSI Technology and Circuits (VLSI 2019), Kyoto, Japan.

賀！本團隊的論文被ESSCIRC 接受
題目："PTLL-BNN: Physically Tightly Coupled, Logically Loosely Coupled, Near-Memory BNN Accelerator"
@2019 IEEE European Solid-State Circuits Conference (ESSCIRC)

賀！本團隊的論文被Nature 接受
題目："CMOS-integrated memristive non-volatile computing-in-memory for AI edge processors"
@Nature Electronics, 2, 420-428, 2019.

射月計劃相關報道

賀！本團隊的論文被ISSCC 2019接受
題目："A 1Mb Multibit ReRAM Computing-In-Memory Macro with 14.6 ns Parallel MAC Computing Time for CNN Based AI Edge Processors"
"A Twin-8T SRAM Computation-In-Memory Macro for Multiple-Bit CNN-Based Machine Learning"

賀！本團隊的論文被ISSCC 2018接受
題目："A 65nm 1Mb nonvolatile computing-in-memory ReRAM macro with sub-16ns multiply-and-accumulate for binary DNN AI edge processors"