| 作成者 |
|
|
|
|
|
| 出版者 |
|
| 発行日 |
|
| 開始ページ |
|
| 終了ページ |
|
| 会議情報 |
|
| アクセス権 |
|
| 権利関係 |
|
|
|
| 関連DOI |
|
|
|
| 関連DOI |
|
| 関連URI |
|
| 関連ISBN |
|
|
|
| 関連HDL |
|
|
|
| 関連情報 |
|
|
|
| 概要 |
Operating IoT devices by supplying power from an energy harvester and installing AI accelerators in IoT devices are required. Nevertheless, conventionally selected architectures for AI processing requ...ire a large amount of power, making it difficult to operate in low-power bands such as IoT devices or even impossible to operate in the first place. Therefore, driving AI accelerators with power that energy harvesters can supply is an issue. However, there has been no past exploration of AI accelerators in the driven of the μW scale. In this paper, we analyze the configuration of Vector Matrix Multiplier, mainly used for AI accelerators, and show the effective configuration for μW scale IoT devices. Using 180nm CMOS to synthesize the four architectures of various sizes, we characterize device performance and analyze energy consumption and circuit area. As a result of the analysis, it shows that the configuration in which all calculations are deployed on the circuit can have the lowest energy consumption. In addition, we found that when there is a limit on circuit area, a configuration in which some calculations are performed in the time domain by lowering the voltage is suitable.続きを見る
|
Mendeley出力
