In the architecture of system-on-chip (SoC) design, the bus plays a critical role by facilitating inter-module connections and managing data transmission. Although the commercial bus solutions represented by the Cortex-M System Design Kit (CMSDK) are widely adopted in the industry, they exhibit lower communication efficiency in certain specialized requirements. This research focuses on optimizing the transition from a 32-bit high-speed intra-core bus to a 64-bit low-speed AHB-Lite bus. A new solution (MergFS) for this conversion process is proposed and implemented, which supports request merging and dynamic frequency switching. Experimental results show that, compared to the solution using CMSDK, MergFS reduces clock cycles by approximately 50% to 75% when processing multiple transactions. Additionally, synthesis results under the three different technologies show MergFS introduces ~4.5% area and ~6.02% power overhead on average.

Current processor chip designs are mainly oriented by performance, power and area (PPA), and developed using the waterfall model. However, there are two main challenges in this development model: 1) The end-to-end iteration cycle and cost of processor chip development are too high, and cannot flexibly respond to changes in chip fragmented design specifications. 2) Processor chip verification is less agile, and there is a lack of a full-chain processor agile design platform that can be easily ported to different development environments. To tackle both issues, we propose an object-oriented hardware agile design methodology, oriented by time, cost, and complexity, and have built the RIVL platform to support the agile development process for processors. RIVL integrates a highly automated design flow for processor RTL design, Integration, Verification, and Layout design to improve processor development efficiency. We achieved tape-out verification of more than 60 RISC-V processors through agile design methods, demonstrating the use and effectiveness of RIVL. We quantify the performance of CoreGen using CoreMark and demonstrate that CoreGen achieves industry-competitive performance.

Partitioning a large graph into smaller subgraphs by minimizing the number of cutting vertices and edges, namely cut size or replication factor, plays a crucial role in distributed graph processing tasks. However, many prior works have primarily focused on optimizing the cut size by considering only vertex balance or edge balance, leading to significant workload imbalance and consequently hindering the performance of downstream tasks. Therefore, in this paper, we address the dual-balanced graph partition problem that minimizes the cut size while simultaneously guaranteeing both vertex and edge balance. We propose a lightweight effective two-phase framework, namely fine-grained splitting and merging (FSM), which decomposes the graph into more and smaller partitions and then merges them. FSM offers the flexibility of integrating with various state-of-the-art single-balanced techniques. We develop two efficient algorithms Fast Merging and Precise Merging to enable trade-offs between computational efficiency and partitioning quality. Experimental results on large real-world graphs demonstrate that FSM achieves state-of-the-art cut size while maintaining dual balance. The runtime for downstream tasks PageRank, connected component, and diameter estimation, can be reduced by a large proportion, up to 9.43%, 11.35%, and 17.94%, respectively.

Machine Learning (ML) research has primarily focused on improving the accuracy and efficiency of the training algorithms while paying much less attention to the equally important problem of understanding, validating, and monitoring the data fed to ML. Irrespective of the ML algorithms used, data errors can adversely affect the quality of the generated model. This indicates that we need to adopt a data-centric approach to ML that treats data as a first-class citizen, on par with algorithms and infrastructure which are the typical building blocks of ML pipelines. In this demonstration we showcase TensorFlow Data Validation (TFDV), a scalable data analysis and validation system for ML that we have developed at Google and recently open-sourced. This system is deployed in production as an integral part of TFX - an end-to-end machine learning platform at Google. It is used by hundreds of product teams at Google and has received significant attention from the open-source community as well.

We look into the issue that the amount of entropy kept by the pseudorandom number generator (PRNG) of Android is constantly low. We find that the accusation against this issue of causing poor performance and low frame rate experienced by users is ungrounded. We also investigate possible security vulnerabilities resulting from this issue. We find that this issue does not affect the quality of random numbers that are generated by the PRNG and used in Android applications because recent Android devices do not lack entropy sources. However, we identify a vulnerability in which the stack canary for all future Android applications is generated earlier than the PRNG is properly setup. This vulnerability makes stack overflow simpler and threats Android applications linked with native code (through NDK) as well as Dalvik VM instances. An attacker could nullify the stack protecting mechanism, given the knowledge of the time of boot or a malicious app running on the victim device. This vulnerability also affects the address space layout randomization (ASLR) mechanism on Android, and can turn it from a weak protection to void. We discuss in this paper several possible attacks against this vulnerability as well as ways of defending. As this vulnerability is rooted in an essential Android design choice since the very first version, it is difficult to fix.

With the rapid development of Social Network (SN for short), people increasingly pay attention to the importance of the roles which they play in the SNs. As is usually the case, the standard for measuring the importance of the members is multi-objective. The skyline operator is thus introduced to distinguish the important members from the entire community. For decision-making, people are interested in the most potential stars which can be promoted into the skyline with minimum cost, namely the problem of Member Promotion in Social Networks. In this paper, based on the characteristic of the skyline operator and the promotion process, we first of all propose some interesting new concepts such as Promotion Boundary to design a novel promotion boundary-based pruning strategy. After that, we bring forward an effective cost-based pruning strategy on the basis of permutation and combination theories to verify the plans in the ascending order of cost. The Sky Boundary algorithm is therefore proposed to solve the problem effectively by employing the optimization strategies. Extensive experiments on both real and synthetic datasets are conducted to show the application value, effectiveness and efficiency of the Sky Boundary algorithm.

Structured P2P network is popular nowadays. However, the network needs reorganized when the peers are joining or leaving. To reduce the performance degradation caused by the reorganizations, we proposed a two-layer hybrid P2P network (HP2P). The upper layer is chord network and the lower layer is cluster in HP2P. In this paper, the cluster is described in detail. Peers are organized into clusters, and the network reorganizations are restricted within the clusters where a peer can join or leave. Besides, each cluster is also enhanced through some specific mechanisms such as Supernode, metadata redundancy and gossip flooding. Consequently, the two layers P2P become more robust. Further, the high efficiency of distributed hash tables(DHTs) can be retained, and the churn caused by excessive node joining and leaving can be reduced.

Unstructured and structured P2P are two typical distributed models for P2P networking. The unstructured P2P uses flooding method, and has poor scalability and low efficiency, while the structured P2P uses DHT (Distributed Hash Table) method, and has low stability. This paper proposes a two-layer hybrid P2P network - HP2P, which combines flooding and DHT methods: Chord is used for the upper layer and flooding for the lower layer. The general framework of HP2P is presented and its upper and lower layers are described. By analyzing the performance of HP2P, it shows that HP2P has well scalability, high efficiency, and good stability.