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

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arxiv 2401.05548 v2 pith:OLN3HYI5 submitted 2024-01-10 cs.AR

classification cs.AR
keywords acceleratorsapplicationsx-heepconsumptionedgeenergyplatformarray
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Decoding Alignment: A Critical Survey of LLM Development Initiatives through Value-setting and Data-centric Lens

    cs.CL 2025-08 unverdicted novelty 6.0 of 10

    FEMU, demonstrated as X-HEEP-FEMU on a Zynq SoC, combines FPGA-based hardware prototyping, software virtualization, and silicon-calibrated energy modeling into one platform for TinyAI design exploration.

  2. MEDEA: A Design-Time Multi-Objective Manager for Energy-Efficient DNN Inference on Heterogeneous Ultra-Low Power Platforms

    cs.AR 2025-06 conditional novelty 6.0 of 10

    MEDEA uses integer linear programming to pick per-kernel processor, voltage/frequency, and tiling decisions that minimize estimated energy under a deadline for DNN inference on heterogeneous ultra-low-power platforms,...

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

    cs.AR 2025-08 conditional novelty 4.0 of 10

    X-HEEP is an open-source RISC-V platform with a flexible accelerator interface, demonstrated with a near-memory early-exit accelerator that yields up to 7.3x speedup and 3.6x energy gains in simulation.

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