Accelerating Edge AI with Morpher: An Integrated Design, Compilation and Simulation Framework for CGRAs

TL;DR

Morpher is an open-source framework that streamlines the design, compilation, and simulation of CGRAs, enabling efficient deployment of AI workloads on power-efficient edge accelerators.

Contribution

It introduces a comprehensive, architecture-adaptive CGRA framework with integrated compiler, simulator, and validation tools for edge AI applications.

Findings

Automates compilation of AI kernels onto custom CGRA architectures

Provides verification tools to ensure functionality of CGRA designs

Facilitates exploration of CGRA design space for edge AI workloads

Abstract

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.