Work-in-Progress: Micro-Accelerator-in-the-Loop Framework for MCU Integrated Accelerator Peripheral Fast Prototyping

Abstract

The resource constraints of MCU-based platforms limits their ability to utilize high-performance accelerators such as GPUs or servers, mainly due to insufficient resources for ML applications. Currently, solutions utilizing accelerators connected as peripherals to the on-chip bus of microcontroller units (MCUs) are being proposed. We define this approach as a Micro-Accelerator (MA). Due to the necessity of connecting the MA to the MCU core and the on-chip bus within the chip, conducting a iterative full system evaluation of the embedded software that drives the MA poses significant challenges. To address this challenge, we propose a framework that enables rapid prototyping of custom-designed MA and facilitates profiling of its acceleration performance. Experimental results evaluating the performance of the MA for two tiny machine learning (TinyML) applications within the proposed framework demonstrate a cycle latency reduction of 84.32% and 61.32% compared to a general machine learning framework, respectively.