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◆ Proceedings of the ACM on Measurement and Analysis of Computing Systems2025-12-01· Computer science

µ-VF: Enabling Virtualization of Embedded FPGAs

Vincenzo Alessio Bucaria, Francesco Longo, Giovanni Merlino, Francesco Restuccia

原始摘要(英文原文)· Original abstract
Despite growing interest in virtualization of Field-Programmable Gate Arrays (FPGAs), existing approaches predominantly target datacenter-class FPGAs, which heavily rely on external (powerful) servers for hypervisor execution and resource management. This significantly limits their suitability for edge environments where autonomy, energy efficiency, and direct low-latency access to physical Input/Output (I/O) are critical. To address this goal, this paper introduces µ-VF, a lightweight virtualization framework specifically designed to enable robust multi-tenancy on embedded FPGAs operating autonomously at the network edge. µ-VF embeds all virtualization logic entirely onboard the FPGA unit, eliminating the need for any off-chip infrastructure and thus significantly reducing overall system power consumption. Each tenant operates within a secure and isolated container on the on-chip Processing System (PS), coupled with exclusive access to a dedicated Programmable Logic (PL) region. Additionally, µ-VF fully virtualizes external General-Purpose Input/Output (GPIO) directly within the PL fabric, thus enabling independent, concurrent and latency-sensitive access to shared peripherals. We have implemented a prototype of µ-VF with a Zynq UltraScale+ ZCU102 board with PL operating at 100 MHz. Experimental results demonstrate that the hardware virtualization layer utilizes less than 10% of the FPGA's logic resources, with 85% available for tenant applications compared to 50% in prior work. Moreover, µ-VF adds 2.93% to Memory-Mapped I/O (MMIO) access latency compared to native execution for single-tenant operation, increasing to 6.5% with four concurrent tenants. Memory throughput measurements show 1.8% overhead for write operations and negligible impact on read operations, with aggregate throughput 17.1% higher than previous frameworks. Hardware-based GPIO remapping completes in 20 nanoseconds.
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