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

Understand

The increasing computational demands of transformer models in time series classification necessitate effective optimization strategies for energy-efficient deployment.

• Our study presents a systematic investigation of optimization techniques, focusing on structured pruning and quantization methods for transformer architectures.
• Through extensive experimentation on three distinct datasets (RefrigerationDevices, ElectricDevices, and PLAID), we quantitatively evaluate model performance and energy efficiency across different transformer configurations.
• Our experimental results demonstrate that static quantization reduces energy consumption by 29.14% while maintaining classification performance, and L1 pruning achieves a 63% improvement in inference speed with minimal accuracy degradation.