e-GPU: An Open-Source and Configurable RISC-V Graphic Processing Unit for TinyAI Applications
Simone Machetti, Pasquale Davide Schiavone, Lara Orlandic, Darong Huang, Deniz Kasap, Giovanni Ansaloni, David Atienza

TL;DR
This paper introduces e-GPU, an open-source, configurable RISC-V GPU platform optimized for TinyAI edge devices, demonstrating significant performance and energy efficiency improvements through integration with a lightweight programming framework and real-world benchmarks.
Contribution
The work presents a novel open-source RISC-V GPU architecture tailored for TinyAI, including a lightweight programming framework and integration with an energy-efficient platform, optimized for ultra-low-power edge applications.
Findings
e-GPU achieves up to 15.1x speed-up on TinyBio workloads.
Energy consumption is reduced by up to 3.1x with high-range configuration.
The system operates within a 28 mW power budget.
Abstract
Graphics processing units (GPUs) excel at parallel processing, but remain largely unexplored in ultra-low-power edge devices (TinyAI) due to their power and area limitations, as well as the lack of suitable programming frameworks. To address these challenges, this work introduces embedded GPU (e-GPU), an open-source and configurable RISC-V GPU platform designed for TinyAI devices. Its extensive configurability enables area and power optimization, while a dedicated Tiny-OpenCL implementation provides a lightweight programming framework tailored to resource-constrained environments. To demonstrate its adaptability in real-world scenarios, we integrate the e-GPU with the eXtendible Heterogeneous Energy-Efficient Platform (X-HEEP) to realize an accelerated processing unit (APU) for TinyAI applications. Multiple instances of the proposed system, featuring varying e-GPU configurations, are…
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