Optimization of Layer Skipping and Frequency Scaling for Convolutional Neural Networks under Latency Constraint
The energy consumption of Convolutional Neural Networks (CNNs) is a critical factor in deploying deep learning models on resource-limited equipment such as mobile devices and autonomous vehicles. We propose an approach involving Proportional Layer Skipping (PLS) and Frequency Scaling (FS). Layer skipping reduces computational complexity by selectively bypassing network layers, whereas frequency scaling adjusts the frequency of the processor to optimize energy use under latency constraints. Experiments of PLS and FS on ResNet-152 with the CIFAR-10 dataset demonstrated significant reductions in computational demands and energy consumption with minimal accuracy loss. This study offers practical solutions for improving real-time processing in resource-limited settings and provides insights into balancing computational efficiency and model performance.
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous VehiclesComputational EfficiencySimilar Papers 제목 키워드 기반
Dual Skipping Networks
Inspired by the recent neuroscience studies on the left-right asymmetry of the human brain in processing low and high spatial frequency information, this paper introduces a dual skipping network which carries out coarse-…
General ClassificationObjectObject CategorizationFractional Skipping: Towards Finer-Grained Dynamic CNN Inference
While increasingly deep networks are still in general desired for achieving state-of-the-art performance, for many specific inputs a simpler network might already suffice. Existing works exploited this observation by lea…
QuantizationAdaSkip: Adaptive Sublayer Skipping for Accelerating Long-Context LLM Inference
Long-context large language models (LLMs) inference is increasingly critical, motivating a number of studies devoted to alleviating the substantial storage and computational costs in such scenarios. Layer-wise skipping m…
ScalingNet: extracting features from raw EEG data for emotion recognition
Convolutional Neural Networks(CNNs) has achieved remarkable performance breakthrough in a variety of tasks. Recently, CNNs based methods that are fed with hand-extracted EEG features gradually produce a powerful performa…
EEGElectroencephalogram (EEG)Emotion RecognitionSkipNet: Learning Dynamic Routing in Convolutional Networks
While deeper convolutional networks are needed to achieve maximum accuracy in visual perception tasks, for many inputs shallower networks are sufficient. We exploit this observation by learning to skip convolutional laye…
Decision MakingReinforcement LearningSequential Decision Making