Name
Abstract | ||
|---|---|---|
Augmented reality (AR) aims to implement applications, requiring high performance, while consuming low power on an all-day wearable, small form-factor, device. Luckily, many AR applications such as neural networks are error-resilient (i.e., results are same with errors in computation or memory), providing an opportunity to utilize low-power circuit techniques when implementing their building block... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/SiPS52927.2021.00051 | 2021 IEEE Workshop on Signal Processing Systems (SiPS) |
Keywords | DocType | ISSN |
error-resilient computing,augmented reality,neural networks,SRAM,low-power | Conference | 1520-6130 |
ISBN | Citations | PageRank |
978-1-6654-0144-9 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tony F. Wu | 1 | 0 | 0.34 |
| Doyun Kim | 2 | 0 | 0.34 |
| Daniel H. Morris | 3 | 0 | 0.34 |
| Edith Beigne | 4 | 536 | 52.54 |