Accelerating Edge AI with Morpher: An Integrated Design, Compilation and Simulation Framework for CGRAs
This work addresses the need for power-efficient and versatile accelerators for edge AI applications, though it appears incremental as it builds on existing CGRA concepts with a new integrated toolset.
The paper tackles the challenge of designing and optimizing Coarse-Grained Reconfigurable Arrays (CGRAs) for edge AI by introducing Morpher, an open-source framework that automates compilation and verification of AI kernels onto user-defined architectures, enabling efficient exploration of the CGRA design space.
Coarse-Grained Reconfigurable Arrays (CGRAs) hold great promise as power-efficient edge accelerator, offering versatility beyond AI applications. Morpher, an open-source, architecture-adaptive CGRA design framework, is specifically designed to explore the vast design space of CGRAs. The comprehensive ecosystem of Morpher includes a tailored compiler, simulator, accelerator synthesis, and validation framework. This study provides an overview of Morpher, highlighting its capabilities in automatically compiling AI application kernels onto user-defined CGRA architectures and verifying their functionality. Through the Morpher framework, the versatility of CGRAs is harnessed to facilitate efficient compilation and verification of edge AI applications, covering important kernels representative of a wide range of embedded AI workloads. Morpher is available online at https://github.com/ecolab-nus/morpher-v2.