Does convolutional neural network face computational demand issues ?
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Convolutional neural networks (CNNs) do face computational demand issues. The computational workload of CNNs can be reduced through algorithmic modifications, resulting in up to a 47% reduction in workload without any change in image recognition results or the need for additional hardware accelerators . The growing amount of data and the increase in model size and complexity pose major computational challenges for CNNs, especially in edge and IoT devices as well as large-scale computing systems. To address this, compression algorithms like S-VELCRO have been introduced, which exploit value locality to trim filters in CNN models and achieve a compression-saving ratio between 6% and 30% without accuracy degradation . Parameter pruning techniques and inference optimization methods are also used to reduce the computational cost of CNNs while examining their effect on performance . Optimized hardware implementations are crucial for real-time CNN applications, and research has focused on reducing computational complexity and hardware resources while maintaining accuracy .
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| Papers (5) | Insight |
|---|---|
| Yes, convolutional neural networks (CNNs) face computational challenges due to the growing amount of data and the increasing model size and complexity (mentioned in the abstract). | |
| Yes, convolutional neural networks (CNNs) face computational demand issues, as mentioned in the abstract of the paper. The paper proposes an algorithmic modification to reduce the computational workload of CNNs. | |
54 Citations | Yes, the convolutional neural network faces challenges of high computational complexity, as mentioned in the abstract of the paper. |
1 Citations | Yes, the paper states that convolutional neural networks (CNNs) involve intensive processing operations and memory bandwidth for achieving desired performance, indicating that they do face computational demand issues. |
01 Oct 2018 17 Citations | Yes, the paper discusses how the computational demands of Convolutional Neural Networks conflict with the limited computing resources of embedded systems. |
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