TYTAN: Accelerating AI Inference with Taylor-series based Activation
Analysis
This paper addresses the critical need for energy-efficient AI inference, especially at the edge, by proposing TYTAN, a hardware accelerator for non-linear activation functions. The use of Taylor series approximation allows for dynamic adjustment of the approximation, aiming for minimal accuracy loss while achieving significant performance and power improvements compared to existing solutions. The focus on edge computing and the validation with CNNs and Transformers makes this research highly relevant.
Key Takeaways
- •Proposes TYTAN, a hardware accelerator for non-linear activation functions.
- •Employs Taylor series approximation for dynamic and efficient computation.
- •Targets energy-efficient AI inference at the edge.
- •Demonstrates significant performance and power improvements over existing solutions (NVDLA).
- •Validated with CNNs and Transformers.
Reference
“TYTAN achieves ~2 times performance improvement, with ~56% power reduction and ~35 times lower area compared to the baseline open-source NVIDIA Deep Learning Accelerator (NVDLA) implementation.”