| Autor: |
Zamirai, Babak, Latifi, Salar, Zamirai, Pedram, Mahlke, Scott |
| Předmět: |
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| Zdroj: |
DAC: Annual ACM/IEEE Design Automation Conference; 2020, Issue 57, p501-506, 6p |
| Abstrakt: |
This paper proposes SIEVE, Speculative Inference on the Edge with Versatile Exportation, which dynamically distributes CNN computation between the cloud and edge device based on the input data and environmental conditions to maximize efficiency and performance. A speculative CNN is created through aggressive precision reduction techniques to run most of the inferences on the edge device, while the original CNN is run on the cloud server. A runtime system directs each input to either the edge or cloud and decides whether to accept speculative inferences made on the edge or invoke recovery by replaying the inference on the cloud. Compared to the cloud-only approach, SIEVE reduces energy consumption by an average of 91%, 57% and 26% and increases performance by an average of 12.3x, 2.8x and 2.0x for 3G, LTE and WiFi connections without accuracy loss across a range of nine CNNs. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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