Recent pruning approaches also consider the targeted device (i.e., graphics processing units) for CNN deployment to reduce the actual inference time. However, simple metrics, such as the 1 -norm, are used for deciding which filters to prune. In this work, we propose a ...
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 ...
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
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...
Table 1 shows the results from four different LMU models. The first model (LMU-1) uses 8-bit weights, while the remaining three models use 4-bit weights. All LMU models use 7-bit activations. LMU-1 and LMU-2 are not pruned. LMU-3 has 80% pruning performed and LMU-4 has 91% of...
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法律...
Keyword spotting is a task that requires ultra-low power due to its always-on operation. State-of-the-art approaches achieve this by drastically pruning model size, yet often at the expense of accuracy. This work tackles this fundamental conflict between operating efficiency and accuracy in three...
Network Pruning for Bit-Serial Accelerators To boost the performance of typical BSA accelerators, we present Bit-Pruner, a software approach to learn BSA-favored NNs without resorting to hardware ... X Zhao,Y Wang,C Liu,... - IEEE Transactions on Computer-Aided Design of Integrated Circuits ...
We then propose a hardware-aware multi-objective Design Space Exploration (DSE) technique for filter pruning that involves the targeted device (i.e., Graphics Processing Units (GPUs)). For each layer, the number of filters to be pruned is optimized with the objectives of minimizing the ...
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 ...