Benefit of pruning and clustering a neural network for before deploying on Arm Ethos-U NPU
July 24, 2023
Pruning and clustering are optimization techniques:
- Pruning: setting weights to zero
- Clustering: grouping weights together into clusters
These techniques modify the weights of a Machine Learning model. In some cases, they enable:
- Significant speed-up of the inference execution
- Reduction of the memory footprint
- Reduction in the overall power consumption of the system
We assume that you can optimize your workload without loss in accuracy and that you target an Arm® Ethos NPU. You can therefore prune and cluster your neural network before using the Vela compiler and deploying it on the Ethos-U hardware. See below for more information on optimizing your workload.
To read the full article, click here
Related Semiconductor IP
- NPU IP Core for Mobile
- NPU IP Core for Edge
- Specialized Video Processing NPU IP
- HYPERBUS™ Memory Controller
- AV1 Video Encoder IP
Related Blogs
- Reviewing different Neural Network Models for Multi-Agent games on Arm using Unity
- Neural Network Model quantization on mobile
- Pace of Innovation for Custom Silicon on Arm Continues with AWS Graviton4
- Windows on Arm is Ready for Prime Time: Native Chrome Caps Momentum for the Future of Laptop Computing
Latest Blogs
- Cadence Extends Support for Automotive Solutions on Arm Zena Compute Subsystems
- The Role of GPU in AI: Tech Impact & Imagination Technologies
- Time-of-Flight Decoding with Tensilica Vision DSPs - AI's Role in ToF Decoding
- Synopsys Expands Collaboration with Arm to Accelerate the Automotive Industry’s Transformation to Software-Defined Vehicles
- Deep Robotics and Arm Power the Future of Autonomous Mobility