Spectrum crowdsensing is a paradigm where participants upload their collected spectrum data to the cloud for extracting analytics. First movers like Microsoft Spectrum Observatory and Electrosense, though with support from leading industry, research, and government, still suffer from sustainability challenges. In this paper, we present Fiesta, a sustainable framework for spectrum crowdsensing. On the technology side, we use federated learning and blockchain to decentralize the data analysis computations. For individual participants, minimal invasion of privacy suppresses concerns regarding large-scale adoption. From organizations’ perspectives, using blockchain avoids single point of failure and enhances the robustness of the entire system against malicious attacks. On the policy side, we propose a reward quantification mechanism to motivate engagement. Potential funding sources to ensure ongoing sustainability are also discussed. We have demonstrated Fiesta through simulation testbeds and real-world deployments with two demo tasks. Results show that Fiesta, as a decentralized framework, can preserve user privacy, enhance system robustness, maintain data fidelity compared with traditional methods, and fairly reward participants. We believe Fiesta is a stepping stone for the future spectrum crowdsensing paradigm.

Emerging three-dimensional (3D) memory technologies, such as the Hybrid Memory Cube (HMC) and High Bandwidth Memory (HBM), provide high-bandwidth and massive memory-level parallelism. With the growing heterogeneity and complexity of computer systems (CPU cores and accelerators, etc.), efficiently integrating emerging memories into existing systems poses new challenges and requires detailed evaluation in a realistic computing environment. In this article, we propose MEG, an open source, configurable, cycle-exact, and RISC-V-based full-system emulation infrastructure using FPGA and HBM. MEG provides a highly modular hardware design and includes a bootable Linux image for a realistic software flow, so that users can perform cross-layer software-hardware co-optimization in a full-system environment. To improve the observability and debuggability of the system, MEG also provides a flexible performance monitoring scheme to guide the performance optimization. The proposed MEG infrastructure can potentially benefit broad communities across computer architecture, system software, and application software. Leveraging MEG, we present two cross-layer system optimizations as illustrative cases to demonstrate the usability of MEG. In the first case study, we present a reconfigurable memory controller to improve the address mapping of standard memory controller. This reconfigurable memory controller along with its OS support allows us to optimize the address mapping scheme to fully exploit the massive parallelism provided by the emerging three-dimensional (3D) memories. In the second case study, we present a lightweight IOMMU design to tackle the unique challenges brought by 3D memory in providing virtual memory support for near-memory accelerators. We provide a prototype implementation of MEG on a Xilinx VU37P FPGA and demonstrate its capability, fidelity, and flexibility on real-world benchmark applications. We hope MEG fills a gap in the space of publicly available FPGA-based full-system emulation infrastructures, specifically targeting memory systems, and inspires further collaborative software/hardware innovations.