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View all- Sponner MWaschneck BKumar A(2024)Adapting Neural Networks at Runtime: Current Trends in At-Runtime Optimizations for Deep LearningACM Computing Surveys10.1145/365728356:10(1-40)Online publication date: 14-May-2024
Pixel 2 | iPhone 8 | Top-1 Acc | Param | Input size | |
---|---|---|---|---|---|
MobileNet | 166.5 \(\text{ms}\) | 32.2 \(\text{ms}\) | 70.9% | 17\(\text{ MB}\) | \(224\times 224\) |
Inception-v4 | 3180 \(\text{ms}\) | 611 \(\text{ms}\) | 80.2% | 171\(\text{ MB}\) | \(229\times 229\) |
Key frame | Inter frame | Ave FPS | mAP | |
---|---|---|---|---|
DFF [25] | 89.8 ms | 19 ms | 44.37 | 69.92 |
ADFF | 19 ms | 19 ms | 52.63 | 69.92 |
DFF+VDS | 89.8 | 19 ms | 44.37 | 70.56 |
DFF+SSI | 89.8 | 18.8 ms | 44.74 | 69.32 |
ADFF+VDS+SSI | 18.8 ms | 18.8 ms | 53.19 | 69.92 |
Input Dim | Output Dim | Kernel | Stride | Filter Num | |
---|---|---|---|---|---|
Conv1 | 300 \(\times\) 500 | 150 \(\times\) 250 | 7 | 2 | 64 |
Conv2 | 150 \(\times\) 250 | 75 \(\times\) 125 | 5 | 2 | 128 |
Conv3 | 75 \(\times\) 125 | 38 \(\times\) 63 | 5 | 2 | 256 |
Conv3_1 | 38 \(\times\) 63 | 38 \(\times\) 63 | 3 | 1 | 256 |
Conv4 | 38 \(\times\) 63 | 19 \(\times\) 32 | 3 | 2 | 512 |
Conv4_1 | 19 \(\times\) 32 | 19 \(\times\) 32 | 3 | 1 | 512 |
Conv5 | 19 \(\times\) 32 | 10 \(\times\) 16 | 3 | 2 | 512 |
Conv5_1 | 10 \(\times\) 16 | 10 \(\times\) 16 | 3 | 1 | 512 |
Conv6 | 10 \(\times\) 16 | 5 \(\times\) 8 | 3 | 2 | 1,024 |
Conv6_1 | 5 \(\times\) 8 | 5 \(\times\) 8 | 3 | 1 | 1,024 |
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