INVESTGATING POWER METRICS OF NEURAL NETWORKS AFTER PRUNING, QUANTIZATION, DPU IMPLEMENTATION: A COMPARATIVE ANALYSIS

Abstract

The rapid proliferation of deep learning applications in various fields has highlighted the need for efficient neural network implementations, especially on resource-constrained edge devices. In response to this demand, pruning and quantization have emerged as essential techniques to reduce the computational and memory requirements of neural networks. Additionally, the deployment of dedicated hardware, such as Digital Processing Units (DPUs), has gained momentum for accelerating neural network inference. This paper presents a comprehensive comparative analysis of the power metrics of neural networks after pruning and quantization, with a particular focus on their implement-ation on DPUs. The objective of this research is to investigate the energy efficiency and power consumption of pruned and quantized neural networks when executed on DPU platforms. The trade-offs between model size reduction and inference accuracy, as well as the power efficiency of different DPU architectures are researched. The results reveal insights into the power efficiency of pruned and quantized neural networks on DPU platforms, offering a clear understanding of the benefits and trade-offs associated with these optimization techniques. This research provides a valuable resource for researchers, developers, and practitioners interested in optimizing neural network implementations for power efficiency. The findings contribute to the ongoing effort to make deep learning more accessible and sustainable on edge devices and other power-constrained environments, ultimately enabling a wider range of applications with reduced energy consumption.