X-HEEP: An Open-Source, Configurable and Extendible RISC-V Microcontroller for the Exploration of Ultra-Low-Power Edge Accelerators

TL;DR

This paper introduces X-HEEP, an open-source, configurable RISC-V microcontroller platform optimized for ultra-low-power edge accelerators, demonstrating significant energy savings in healthcare applications.

Contribution

The paper presents X-HEEP, a novel extendible platform supporting custom low-power accelerators, with real-world FPGA and silicon implementations for healthcare applications.

Findings

Achieved 4.9x energy savings with CGRA accelerators.

Achieved 4.8x energy savings with IMC accelerators.

Demonstrated platform's configurability and real-world applicability.

Abstract

The field of edge computing has witnessed remarkable growth owing to the increasing demand for real-time processing of data in applications. However, challenges persist due to limitations in performance and power consumption. To overcome these challenges, heterogeneous architectures have emerged that combine host processors with specialized accelerators tailored to specific applications, leading to improved performance and reduced power consumption. However, most of the existing platforms lack the necessary configurability and extendability options for integrating custom accelerators. To overcome these limitations, we introduce in this paper the eXtendible Heterogeneous Energy-Efficient Platform (X-HEEP). X-HEEP is an open-source platform designed to natively support the integration of ultra-low-power edge accelerators. It provides customization options to match specific application requirements by exploring various core types, bus topologies, addressing modes, memory sizes, and peripherals. Moreover, the platform prioritizes energy efficiency by implementing low-power strategies, such as clock-gating and power-gating. We demonstrate the real-world applicability of X-HEEP by providing an integration example tailored for healthcare applications that includes a coarse-grained reconfigurable array (CGRA) and in-memory computing (IMC) accelerators. The resulting design, called HEEPocrates, has been implemented both in field programmable gate array (FPGA) on the Xilinx Zynq-7020 chip and in silicon with TSMC 65nm low-power CMOS technology. We run a set of healthcare applications and measure their energy consumption to demonstrate the alignment of our chip with other state-of-the-art microcontrollers commonly adopted in this domain. Moreover, we present the energy benefits of 4.9x and 4.8x gained by exploiting the integrated CGRA and IMC accelerators compared to running on the host CPU.