GVSoC: A Highly Configurable, Fast and Accurate Full-Platform Simulator for RISC-V based IoT Processors
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Abstract (arXiv)
The last few years have seen the emergence of IoT processors: ultra-low power systems-on-chips (SoCs) combining lightweight and flexible micro-controller units (MCUs), often based on open-ISA RISC-V cores, with application-specific accelerators to maximize performance and energy efficiency. Overall, this heterogeneity level requires complex hardware and a full-fledged software stack to orchestrate the execution and exploit platform features. For this reason, enabling agile design space exploration becomes a crucial asset for this new class of low-power SoCs. In this scenario, high-level simulators play an essential role in breaking the speed and design effort bottlenecks of cycle-accurate simulators and FPGA prototypes, respectively, while preserving functional and timing accuracy. We present GVSoC, a highly configurable and timing-accurate event-driven simulator that combines the efficiency of C++ models with the flexibility of Python configuration scripts. GVSoC is fully open-sourced, with the intent to drive future research in the area of highly parallel and heterogeneous RISC-V based IoT processors, leveraging three foundational features: Python-based modular configuration of the hardware description, easy calibration of platform parameters for accurate performance estimation, and high-speed simulation. Experimental results show that GVSoC enables practical functional and performance analysis and design exploration at the full-platform level (processors, memory, peripherals and IOs) with a speed-up of 2500x with respect to cycle-accurate simulation with errors typically below 10% for performance analysis.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jan 2022.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1; 2026: 2.
6 papers.
- 2022Energy-efficient and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inferencecited by 2
- 2025Lightweight Software Kernels and Hardware Extensions for Efficient Sparse Deep Neural Networks on Microcontrollerscited by 2
- 2023FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentationcited by 0
- 2024Accelerating Depthwise Separable Convolutions on Ultra-Low-Power Devicescited by 0
- 2026ALADIN: Accuracy-Latency-Aware Design-space Inference Analysis for Embedded AI Acceleratorscited by 0
- 2026At-the-Roofline Sparse Tensor Contractions on Vector Processors for Transformer Inferencecited by 0
- 2026At-the-Roofline Sparse Tensor Contractions on Vector Processors for Transformer Inference28 Jul 2026
- 2026ALADIN: Accuracy-Latency-Aware Design-space Inference Analysis for Embedded AI Accelerators12 Feb 2026
- 2025Lightweight Software Kernels and Hardware Extensions for Efficient Sparse Deep Neural Networks on Microcontrollers8 Mar 2025
- 2024Accelerating Depthwise Separable Convolutions on Ultra-Low-Power Devices18 Jun 2024
- 2023FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation28 Mar 2023
- 2022Energy-efficient and Privacy-aware Social Distance Monitoring with Low-resolution Infrared Sensors and Adaptive Inference22 Apr 2022