Abstract
Convolutional neural networks (CNNs) often extract similar features from successive video frames due to having identical appearances. In contrast, conventional CNNs for video recognition process individual frames with a fixed computational effort. Each video frame is independently processed, resulting in numerous redundant computations and an inefficient use of limited energy resources, particularly for edge computing applications. To alleviate the high energy requirements associated with video frame processing, this paper presented similarity-aware CNNs that recognise similar feature pixels across frames and avoid computations on them. First, with a loss of less than 1% in recognition accuracy, a proposed similarity aware quantization technique increases the average number of unchanged feature pixels across frame pairs by up to 85%. Then, a proposed similarity-aware dataflow improves energy consumption by minimising redundant computations and memory accesses across frame pairs. According to simulation experiments, the proposed dataflow decreases the energy consumed by video frame processing by up to 30%.
| Original language | English |
|---|---|
| Journal | Ieee transactions on computer-Aided design of integrated circuits and systems |
| Early online date | 20 Dec 2021 |
| DOIs | |
| Publication status | E-pub ahead of print - 20 Dec 2021 |
Keywords
- Computational modeling
- Convolutional neural networks
- Deep neural networks
- Energy consumption
- Memory management
- Object Detection
- Quantization
- Quantization (signal)
- System-on-chip
- Tensors
- Video Recognition.