Benefit of pruning and clustering a neural network for before deploying on Arm Ethos-U NPU
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
- Band-Gap Voltage Reference with dual 2µA Current Source - X-FAB XT018
- 250nA-88μA Current Reference - X-FAB XT018-0.18μm BCD-on-SOI CMOS
- UCIe D2D Adapter & PHY Integrated IP
- Low Dropout (LDO) Regulator
- 16-Bit xSPI PSRAM PHY
Related Blogs
- Reviewing different Neural Network Models for Multi-Agent games on Arm using Unity
- Neural Network Model quantization on mobile
- Silicon-proven LVTS for 2nm: a new era of accuracy and integration in thermal monitoring
- Area, Pipelining, Integration: A Comparison of SHA-2 and SHA-3 for embedded Systems.
Latest Blogs
- AI in Design Verification: Where It Works and Where It Doesn’t
- PCIe 7.0 fundamentals: Baseline ordering rules
- Ensuring reliability in Advanced IC design
- A Closer Look at proteanTecs Health and Performance Management Solutions Portfolio
- Enabling Memory Choice for Modern AI Systems: Tenstorrent and Rambus Deliver Flexible, Power-Efficient Solutions