A CNN/MLP Neural Processing Engine, Powered by Novel Temporal-Carry-deferring MACs
| dc.contributor.author | Mirzaeian, Ali | |
| dc.date.accessioned | 2021-10-08T17:58:48Z | |
| dc.date.available | 2021-10-08T17:58:48Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | The applications of machine learning algorithms are innumerable and cover nearly every domain of modern technology. During this rapid growth of this area, more and more companies have expressed a desire to utilize machine learning techniques in smaller devices, such as cell phones or smart Internet of Things (IoT) instruments. However, as machine learning has so far required a power source with more capacity and higher efficiency than a conventional battery. Therefore, introducing neural network accelerators with low energy demands and low latency for executing machine learning techniques has drawn lots of attention in both the academia and industry. | |
| dc.identifier.uri | https://hdl.handle.net/1920/12112 | |
| dc.language.iso | en_US | |
| dc.rights | Attribution-ShareAlike 3.0 United States | |
| dc.rights.uri | https://creativecommons.org/licenses/by-sa/3.0/us/ | |
| dc.subject | Neural networks | |
| dc.title | A CNN/MLP Neural Processing Engine, Powered by Novel Temporal-Carry-deferring MACs | |
| dc.type | Technical Report |