Blockchain storage platforms reward storage nodes for keeping user-uploaded data for a certain amount of time. These storage nodes are unstable and can go online or offline unpredictably at any time, leading to potential data loss. To prevent data loss, blockchain storage platforms adopt erasure codes on user-uploaded encrypted data. Data repair processes will be performed to recover the lost data. However, the data repair processes heavily rely on time-consuming erasure coding algorithms, mainly consisting of vector-matrix multiplications. The emerging processing-in-memory technique can efficiently speed up the processing of vector-matrix multiplications. It can be integrated into blockchain storage platforms to solve the data repair issue. This paper presents Rapper, a parameter-aware repair-inmemory accelerator for blockchain storage platforms. Rapper utilizes the computing power of emerging processing-in-memory architecture so that data repair processes can be processed in a parallel manner and the overall efficiency can be improved significantly. Specifically, at the hardware level, the ReRAM memory is reorganized into our proposed double bank, XRU, XGroup, and ReRAM crossbars structure. At the software level, a parallel decoding/encoding strategy is proposed to fully exploit the internal parallelism of ReRAM. We also propose an adaptive parameter-aware mapping to handle various sizes of stripes. To demonstrate the viability of the proposed technique, a representative blockchain storage project Storj is adopted as the default storage infrastructure. Experimental results show that Rapper can achieve a 1.96 × speedup on average compared to the representative scheme.

Transforming physical surfaces into virtual interfaces can extend the interaction capability of many exciting metaverse applications in the future. Recent advances in vibration-based tap sensing show promise for this vision using passive vibration signals. However, current approaches based on Time-Difference-of-Arrival (TDoA) triangulation suffer the impact of fluctuant wave velocity due to the dispersive and heterogeneous nature of solid mediums, failing to meet the performance requirement for practical use. In this article, we present MM-Tap, a vibration-based tap localization system that can transform ubiquitous surfaces into virtual touch screens with low overhead. A novel localization scheme is proposed based on the finding of spatiotemporal mapping between tap locations and TDoA values, which pushes the accuracy limits of vibration-based tap sensing from unstable cm-level to mm-level. We investigate the geometry of the sensor layout and design a model-based method to synthesize tap data, which enables MM-Tap to adapt to various surface materials and respond to arbitrary sensing scales after a few seconds of calibration. We combine MM-Tap with a COTS projector and facilitate a digitally augmented surface where users can play video games with low latency.