Filter pruning is an efficient approach for deep CNN compression and acceleration, which aims to eliminate some filters with tolerable performance degradation. In the literature, the majority of approaches prune networks by defining the redundant filters or training the networks with a sparsity prior ...
The commonly used hardware-aware network compression techniques address this question with pruning, kernel fusion, quantization and lowering precision. However, these approaches do not change the underlying network operations. In this paper, we propose hardware-aware network transformation (HANT), which ...
such as quantization, pruning, and knowledge distillation. Now INC quantization, including both static quantization and dynamic quantization, is available in Olive. Learn more byreading this exampleand ourblog. More compression techniques will
Model Compressor: automatic compression, structured pruning, filter decomposition, & HW aware model profiling Model Launcher: quantization, packaging, converting, & device farm. NetsPresso®'s compression technology is compatible with STM32 Model Zoo and STM32 Cube.AI Developer Cloud ...
Differentiable neural network pruning to enable smart applications on microcontrollers. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.[SPACE]https://doi.org/10.1145/3569468 (2023). Article MATH Google Scholar Liu, C.-L., Hsaio, W.-H. & Tu, Y.-C. Time series classification with ...
Network pruning via transformable architecture search. In: Proceedings of Annual Conference on Neural Information Processing Systems 2019, Vancouver, 2019. 759--770. Google Scholar [8] Iandola F N, Moskewicz M W, Ashraf K, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters...
The increasing size of language models necessitates a thorough analysis across multiple dimensions to assess trade-offs among crucial hardware metrics such as latency, energy consumption, GPU memory usage, and performance. Identifying optimal model configurations under specific hardware constraints is becomin...
Nev- determine the bitwidths for different layers, while our frame- ertheless, these methods are still rule-based and mostly focus work automates this design process, and our learning-based on pruning. Our framework automates the quantization pro- policy outperforms ...
Multi-objective optimizationEvolutionary algorithmNeural network pruningHardware-aware machine learningHardware efficiency[Display omitted]doi:10.1016/j.fmre.2022.07.013Wenjing HongGuiying LiShengcai LiuPeng YangKe TangElsevier B.V.Fundamental Research
Given a trained network, how can we accelerate it to meet efficiency needs for deployment on particular hardware? The commonly used hardware-aware network compression techniques address this question with pruning, kernel fusion, quantization and lowering precision. However, these approaches do not ...