賀!本團隊的論文被IEEE Transactions on Circuits and Systems接受
題目:A 0.3-V Conductance-Based Silicon Neuron in 0.18 µm CMOS Process
Posted by NBME on 04/12, 2021
賀!本團隊的論文被IEEE Journal of Solid State Circuits接受
題目:A Local Computing Cell and 6T SRAM based Computing-in-Memory
Macro
with
8b
MAC
Operation for Edge AI Chips
Posted by NBME on 03/31, 2021
賀!本團隊的論文被AICAS 2021接受
題目:Integer Quadratic Integrate-and-Fire (IQIF): A Neuron Model
for
Digital
Neuromorphic Systems
題目:A Bio-Inspired Motion Detection Circuit for the Computation
of
Optical
Flow:
The Spatial-Temporal Filtering Reichardt Model
本團隊論文發表成功,被收錄於 IEEE Journal of
Solid
State
Circuits 題目是:"A 0.5V Real-Time
Computational
CMOS
Image Sensor with Programmable Kernel for
Feature
Extraction"
Posted by NBME on 10/27,
2020
本團隊論文發表成功,被收錄於 Nature
Electronics
題目是:"A CMOS-integrated compute-in-memory
macro
based
on
resistive random-access memory for AI
edge
devices"
Posted by NBME on
09/26,
2020
射月計畫第二年成果發表會
Posted by NBME on
09/20,
2020
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!
Posted by NBME on
08/17,
2020
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
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"
Posted by NBME on
02/20,
2020
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.
Posted by NBME on
05/17,
2020
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 "
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
Posted by NBME on
12/1,
2019
賀!本團隊入圍參加未來科技展!
English Version
Posted by NBME on
11/20,
2019
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)
Posted by NBME on
11/20,
2019
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.
Posted by NBME on
11/20,
2019
賀!本團隊和Qualcomm開展技術合作
Posted by NBME on
11/14,
2019
本實驗室參與的研究成果被廣泛報道
Posted by NBME on
11/13,
2019
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
Posted by NBME on
11/10,
2019
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"