With the growing memory requirements of modern data-intensive applications for high performance and large capacity, building hybrid memory systems with different memory technologies has become a dominant trend to satisfy these demands. For better system performance, frequently accessed hot data is fetched into the fast and capacity-limited near memory (NM) while cold data is evicted to the slow and large far memory (FM). In prior works, NM is used as a cache of FM (cNM), part of OS-visible memory (mNM), and a fixed capacity of cNM and mNM. This article presents Phoenix, a novel hybrid memory architecture that harnesses the advantages of both cNM and mNM. The ratio of cNM to mNM is adjustable during runtime to better exploit both temporal and spatial locality benefits for different memory access patterns. All cNM and mNM space is multiplexed to mitigate the data movement overhead for the mode switch between cNM and mNM. In our evaluations, Phoenix outperforms state-of-the-art designs by an average of 18.2% and consumes orders of magnitude less metadata storage space.

Regular path queries (RPQs) in graph databases are bottlenecked by the memory wall. Emerging processing-in-memory (PIM) technologies offer a promising solution to dispatch and execute path matching tasks in parallel within PIM modules. We present an efficient PIM-based data management system tailored for RPQs and graph updates. Our solution, called PimBeam, facilitates efficient batch RPQs and graph updates by implementing a PIM-friendly dynamic graph partitioning algorithm. This algorithm effectively addresses graph skewness issues while maintaining graph locality with low overhead for handling RPQs. PimBeam streamlines label filtering queries by adding a filtering module on the PIM side and leveraging the parallelism of PIM. For the graph updates, PimBeam enhances processing efficiency by amortizing the host CPU's update overhead to PIM modules. Evaluation results of PimBeam indicate 3.59x speedup for RPQs and 29.33x speedup for graph update on average over the state-of-the-art traditional graph database.

Regular path queries (RPQs) in graph databases are bottlenecked by the memory wall. Emerging processing-in-memory (PIM) technologies offer a promising solution to dispatch and execute path matching tasks in parallel within PIM modules. We present Moctopus, a PIM-based data management system for graph databases that supports efficient batch RPQs and graph updates. Moctopus employs a PIM-friendly dynamic graph partitioning algorithm, which tackles graph skewness and preserves graph locality with low overhead for RPQ processing. Moctopus enables efficient graph update by amortizing the host CPU's update overhead to PIM modules. Evaluation of Moctopus demonstrates superiority over the state-of-the-art traditional graph database.

Decentralized federated learning (DFL) uses peer-topeer communication to avoid the single point of failure problem in federated learning and has been considered an attractive solution for machine learning tasks on distributed devices. We provide the first solution to a fundamental network problem of DFL: what overlay network should DFL use to achieve fast training of highly accurate models, low communication, and decentralized construction and maintenance? Overlay topologies of DFL have been investigated, but no existing DFL topology includes decentralized protocols for network construction and topology maintenance. Without these protocols, DFL cannot run in practice. This work presents an overlay network, called FedLay, which provides fast training and low communication cost for practical DFL. FedLay is the first solution for constructing near-random regular topologies in a decentralized manner and maintaining the topologies under node joins and failures. Experiments based on prototype implementation and simulations show that FedLay achieves the fastest model convergence and highest accuracy on real datasets compared to existing DFL solutions while incurring small communication costs and being resilient to node joins and failures.

Pointer chasing becomes the performance bottleneck for today's in-memory indexes due to the memory wall. Emerging processing-in-memory (PIM) technologies are promising to mitigate this bottleneck, by enabling low-latency memory access and aggregated memory bandwidth scaling with the number of PIM modules. Prior PIM-based indexes adopt a fixed granularity to partition the key space and maintain static heights of skiplist nodes among PIM modules to accelerate index operations on skiplist, neglecting the changes in skewness and hotness of data access patterns during runtime. In this article, we present RADAR, an innovative PIM-friendly skiplist that dynamically partitions the key space among PIM modules to adapt to varying skewness. An offline learning-based model is employed to catch hotness changes to adjust the heights of skiplist nodes. In multiple datasets, RADAR achieves up to 198.2x performance improvement and consumes 47.4% less memory than state-of-the-art designs on real PIM hardware.

Emerging die-stacked memories can provide higher bandwidth than traditional off-chip DRAM and serve as an off-chip DRAM cache or part of OS-visible memory (POM). This paper presents Bumblebee, a new hybrid memory architecture combining the advantages of both DRAM cache and POM. The ratio of DRAM cache to POM is adjustable in real time to better exploit both temporal and spatial locality benefits for different memory access patterns. Our evaluations indicate at least 35.2% performance improvement and 10.9% ∼ 20.1% less memory dynamic energy consumption for Bumblebee over state-of-the-art designs, as well as orders of magnitude less metadata storage space.

Decentralized federated learning (DFL) has been proposed to use peer-to-peer communication for model aggregation to avoid the single point of failure problem in federated learning (FL). However, this process is vulnerable to attackers who share false models and data. In this work, we propose Blockchain-based Verifiable Decentralized Federated Learning (BVDFL), which leverages a blockchain for decentralized model verification and auditing. BVDFL includes an auditor committee for model verification, a reputation model to evaluate the trustworthiness of clients, and a protocol suite for dynamic network updates. Simulation results show that, with the reputation mechanism, BVDFL achieves fast model convergence and high accuracy on real datasets with malicious clients in the system.

With the boost of computing power in mobile devices and availability of cloud APIs in recent years, mobile augmented reality (AR) applications have become increasingly embedded in people's everyday life. However, effective and intuitive interaction between virtual and real worlds in these AR applications is still an open question. In this paper, we make one step towards an answer by exploring the possibility of incorporating two input modalities, gesture and speech, for enhancing user experience in AR applications.