Title
TIMAQ: A Time-Domain Computing-in-Memory-Based Processor Using Predictable Decomposed Convolution for Arbitrary Quantized DNNs
Abstract
Energy-efficient processors are crucial for accelerating deep neural networks (DNNs) on edge devices with limited battery capacity. To reduce energy consumption, time-domain computing-in-memory (TD-CIM) is a splendid architecture, which consumes low computation and memory access energy due to low toggle rate of time-based signals and less data movements, respectively. When deploying DNNs in TD-CIM...
Year
DOI
Venue
2021
10.1109/JSSC.2021.3095232
IEEE Journal of Solid-State Circuits
Keywords
DocType
Volume
Indexes,Kernel,Convolution,Quantization (signal),Time-domain analysis,Memory management,Delays
Journal
56
Issue
ISSN
Citations 
10
0018-9200
0
PageRank 
References 
Authors
0.34
0
15
Name
Order
Citations
PageRank
Jianxun Yang1122.80
Yuyao Kong211.37
Qing Zhang311432.59
Zhuangzhi Liu400.34
Jing Zhou511.37
Yiqi Wang600.34
Yonggang Liu752.83
Chenfu Guo800.34
te hu922.07
Congcong Li1000.34
leibo liu11816116.95
Jin Zhang1200.34
Shaojun Wei13555102.32
Jun Yang1414736.54
shouyi yin1557999.95