Name
Title | ||
|---|---|---|
Exploiting Inherent Error Resiliency of Deep Neural Networks to Achieve Extreme Energy Efficiency Through Mixed-Signal Neurons |
Abstract | ||
|---|---|---|
Neuromorphic computing, inspired by the brain, promises extreme efficiency for certain classes of learning tasks, such as classification and pattern recognition. The performance and power consumption of neuromorphic computing depend heavily on the choice of the neuron architecture. Digital neurons (Dig-N) are conventionally known to be accurate and efficient at high speed while suffering from high... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TVLSI.2019.2896611 | IEEE Transactions on Very Large Scale Integration (VLSI) Systems |
Keywords | Field | DocType |
Neurons,Computer architecture,Computational modeling,Bandwidth,Brain modeling,Resilience,Transistors | Noise power,Convolutional neural network,Efficient energy use,Computer science,Neuromorphic engineering,Electronic engineering,CMOS,Bandwidth (signal processing),Mixed-signal integrated circuit,Transistor | Journal |
Volume | Issue | ISSN |
27 | 6 | 1063-8210 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baibhab Chatterjee | 1 | 15 | 7.68 |
| Priyadarshini Panda | 2 | 42 | 7.40 |
| Shovan Maity | 3 | 48 | 13.32 |
| Ayan Biswas | 4 | 66 | 6.96 |
| Kaushik Roy | 5 | 239 | 20.51 |
| Shreyas Sen | 6 | 337 | 54.39 |