X-HEEP: An Open-Source, Configurable and Extendible RISC-V Platform for TinyAI Applications

Abstract

X-HEEP is an open-source, configurable RISC-V platform specifically designed for ultra-low-power edge applications (TinyAI). Its key innovation is the eXtendible Accelerator InterFace (XAIF), which enables seamless, highly customizable integration of diverse accelerators, alongside extensive configurability of core components. Implemented in 65 nm technology, X-HEEP achieves a minimal 0.15 mm² footprint and demonstrated a 7.3x performance speedup when integrated with near-memory accelerators for dynamic network applications.

Report

Key Highlights

• Platform Focus: X-HEEP is an open-source, highly configurable RISC-V platform targeting ultra-low-power edge and TinyAI applications.
• Key Innovation: Introduction of the eXtendible Accelerator InterFace (XAIF) to facilitate the seamless integration of various application-specific accelerators.
• High Efficiency: Achieves a minimal footprint of only 0.15 mm² and consumes just 29 µW of leakage power when implemented in 65 nm CMOS technology.
• Performance Gains: A study integrating near-memory accelerators showed significant efficiency improvements, achieving up to 7.3x performance speedup and 3.6x energy improvement over CPU-only execution.

Technical Details

• Architecture: Based on the RISC-V instruction set architecture.
• Accelerator Integration: Uses the proprietary eXtendible Accelerator InterFace (XAIF) designed to handle varying accelerator requirements.
• Configurability: Allows extensive internal configuration of cores, memory subsystem, bus, and peripherals.
• Development Flows: Supports three primary development flows: FPGA prototyping, ASIC implementation, and mixed SystemC-RTL modeling, enabling efficient design space exploration.
• Implementation Specs: Fabricated using TSMC's 65 nm CMOS technology.
• Operating Conditions: Tested at 300 MHz clock speed and 0.8 V operating voltage.
• Demonstration: Tested integration with near-memory accelerators optimized for early-exit dynamic network applications.

Implications

• Accelerating TinyAI Adoption: By providing an open-source, verified, and ultra-low-power base platform, X-HEEP lowers the barrier to entry for developing specialized AI hardware for extreme edge constraints.
• Fostering Heterogeneity: The XAIF standard simplifies the development and integration of specialized accelerators, which is crucial for achieving high energy efficiency in AI inference where standard CPUs fall short.
• Flexibility and Optimization: Support for multiple development flows (ASIC, FPGA, SystemC-RTL) means developers can rapidly prototype and optimize system configurations before committing to expensive fabrication, speeding up the design cycle.
• RISC-V Ecosystem Growth: X-HEEP provides a robust, real-world configurable implementation that strengthens the RISC-V position as the architecture of choice for customized, application-specific embedded computing.