Embedded GPU: An Open-Source And Configurable RISC-V GPU Platform for TinyAI Devices (EPFL) - Semiconductor Engineering

Abstract

EPFL has introduced the Embedded GPU (eGPU), an open-source and highly configurable GPU platform based on the RISC-V instruction set architecture. This innovation is specifically designed to accelerate graphics and computation tasks necessary for resource-constrained environments, such as TinyAI devices. The configurable and open-source nature promotes tailored hardware development and lowers the barrier to entry for specialized silicon integration within the low-power computing ecosystem.

Report

Key Highlights

• Platform Introduction: The Embedded GPU (eGPU) is a newly developed GPU platform originated from EPFL.
• Open Standard: The entire platform is open-source, promoting transparency and collaborative development.
• Target Architecture: It is specifically designed for integration with RISC-V processors, addressing the need for dedicated acceleration hardware in this ISA ecosystem.
• Configurability: A key feature is its high configurability, allowing developers to tailor the GPU's size and performance characteristics for specific application demands (e.g., area efficiency, power savings).
• Primary Use Case: The eGPU is optimized to provide essential acceleration for demanding tasks in resource-constrained environments, particularly TinyAI devices and edge computing.

Technical Details

• Optimization Goals: The architecture is primarily optimized for low power consumption and a minimal silicon footprint, vital characteristics for deployment in 'TinyAI' and IoT devices.
• Processing Capabilities: Designed to handle both graphics rendering pipelines and the vector/matrix math operations crucial for efficient neural network inference.
• RISC-V Integration: The platform must provide an efficient interface and instruction set compliance to work seamlessly with host RISC-V cores, likely leveraging specialized RISC-V vector extensions for maximum throughput.
• Scalable Design: Its configurable nature implies a modular design structure that allows scaling the number of processing units (e.g., shader cores) and adjusting memory interfaces based on performance needs versus available die area.

Implications

• Filling a Market Gap: The eGPU provides a standardized, high-performance, and non-proprietary GPU option for the burgeoning RISC-V ecosystem, which traditionally lacked strong open-source GPU IP.
• Boosting TinyAI Adoption: By offering scalable, dedicated acceleration for deep learning, the platform enables more complex and efficient AI models to run directly on extremely low-power edge devices, accelerating the growth of TinyAI.
• Encouraging Custom Silicon: The open-source licensing encourages rapid prototyping and customization, allowing companies to create highly differentiated silicon solutions optimized precisely for their unique AI or graphics workload, without proprietary licensing fees.
• Strengthening RISC-V Competitiveness: The availability of high-quality, open compute accelerators like the eGPU significantly enhances RISC-V's overall viability and competitiveness against established ISAs in graphics and AI-driven markets.