Name
Title | ||
|---|---|---|
A Neuromorphic Chip Optimized for Deep Learning and CMOS Technology With Time-Domain Analog and Digital Mixed-Signal Processing. |
Abstract | ||
|---|---|---|
Demand for highly energy-efficient coprocessor for the inference computation of deep neural networks is increasing. We propose the time-domain neural network (TDNN), which employs time-domain analog and digital mixed-signal processing (TDAMS) that uses delay time as the analog signal. TDNN not only exploits energy-efficient analog computing, but also enables fully spatially unrolled architecture b... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/JSSC.2017.2712626 | IEEE Journal of Solid-State Circuits |
Keywords | Field | DocType |
Convolution,Time-domain analysis,Computer architecture,Training,Neural networks,Kernel,Neuromorphics | Analogue electronics,Computer science,Neuromorphic engineering,Chip,Electronic engineering,CMOS,Time delay neural network,Analog signal,Mixed-signal integrated circuit,Analog computer | Journal |
Volume | Issue | ISSN |
52 | 10 | 0018-9200 |
Citations | PageRank | References |
12 | 1.01 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daisuke Miyashita | 1 | 72 | 9.99 |
| Shouhei Kousai | 2 | 127 | 18.37 |
| Tomoya Suzuki | 3 | 24 | 3.37 |
| jun deguchi | 4 | 15 | 2.49 |