Energy-Efficient Transformer Inference: Optimization Strategies for Time Series Classification

Despite their effectiveness, models in time series classification necessitate transformers are computationally expensive, effective optimization strategies for energyefficient making them less viable for real-time and edge-based deployment. Our study presents a systematic applications due to their high energy consumption investigation of optimization techniques, focusing and memory footprint. Furthermore, the growing on structured pruning and quantization methods carbon footprint associated with transformer training for transformer architectures. Through extensive and inference has raised significant concerns regarding experimentation on three distinct datasets (RefrigerationDevices, sustainability and deployment feasibility in ElectricDevices, and PLAID), we resource-constrained environments [22]. With the exponentially quantitatively evaluate model performance and energy increasing usage of AI, the carbon footprint efficiency across different transformer configurations. of massive models has been a topic of increasing Our experimental results demonstrate that worry. At the same time, deep learning model's runaway static quantization reduces energy consumption by scaling, such as huge transformers and diffusion 29.14% while maintaining classification performance, models, has induced unmatched computation costs, and L1 pruning achieves a 63% improvement in inference which are enormous and require a lot of power to operate speed with minimal accuracy degradation. Our and train, directly contributing to the increase findings provide valuable insights into the effectiveness in global carbon emissions.