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
- DSP-Based 112G SerDes
- XTAL oscillator in TSMC-7nm
- GPU
- V-by-One Verification IP
- AI model compression IP
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
- World's First Standards-Compliant 112G PHY IP for Linear Optics: A Turning Point for AI Interconnects
- One Key for Every Door: How Aliro Extends the UWB Digital Key Beyond the Car
- Reprogrammable Post-Quantum Security for SoCs: Why Crypto-Agility Matters
- Designing the Beam Steering Core for a C-Band AESA: A Look at VSI's VBF0644 GaAs Beamformer IC
- Secure Boot for embedded systems: Building a complete chain of trust