Monad: Towards Cost-effective Specialization for Chiplet-based Spatial Accelerators

Advanced packaging offers a new design paradigm in the post-Moore era, where many small chiplets can be assembled into a large system. Based on heterogeneous integration, a chiplet-based accelerator can be highly specialized for a specific workload, demonstrating extreme efficiency and cost reduction. To fully leverage this potential, it is critical to explore both the architectural design space for individual chiplets and different integration options to assemble these chiplets, which have yet to be fully exploited by existing proposals. This paper proposes Monad, a cost-aware specialization approach for chiplet-based spatial accelerators that explores the tradeoffs between PPA and fabrication costs. To evaluate a specialized system, we introduce a modeling framework considering the non-uniformity in dataflow, pipelining, and communications when executing multiple tensor workloads on different chiplets. We propose to combine the architecture and integration design space by uniformly encoding the design aspects for both spaces and exploring them with a systematic ML-based approach. The experiments demonstrate that Monad can achieve an average of 16% and 30% EDP reduction compared with the state-of-the-art chiplet-based accelerators, Simba and NN-Baton, respectively.Comment: To be published in ICCAD 202

BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection

Graph anomaly detection (GAD) has gained increasing attention in recent years due to its critical application in a wide range of domains, such as social networks, financial risk management, and traffic analysis. Existing GAD methods can be categorized into node and edge anomaly detection models based on the type of graph objects being detected. However, these methods typically treat node and edge anomalies as separate tasks, overlooking their associations and frequent co-occurrences in real-world graphs. As a result, they fail to leverage the complementary information provided by node and edge anomalies for mutual detection. Additionally, state-of-the-art GAD methods, such as CoLA and SL-GAD, heavily rely on negative pair sampling in contrastive learning, which incurs high computational costs, hindering their scalability to large graphs. To address these limitations, we propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE). We extract a subgraph (graph view) centered on each target node as node context and transform it into a dual hypergraph (hypergraph view) as edge context. These views are encoded using graph and hypergraph neural networks to capture the representations of nodes, edges, and their associated contexts. By swapping the context embeddings between nodes and edges and measuring the agreement in the embedding space, we enable the mutual detection of node and edge anomalies. Furthermore, we adopt a bootstrapped training strategy that eliminates the need for negative sampling, enabling BOURNE to handle large graphs efficiently. Extensive experiments conducted on six benchmark datasets demonstrate the superior effectiveness and efficiency of BOURNE in detecting both node and edge anomalies

Scaling Attributed Network Embedding to Massive Graphs

Given a graph G where each node is associated with a set of attributes, attributed network embedding (ANE) maps each node vin G to a compact vector Xv, which can be used in downstream machine learning tasks. Ideally, Xv should capture node v's affinity to each attribute, which considers not only v's own attribute associations, but also those of its connected nodes along edges in G. It is challenging to obtain high-utility embeddings that enable accurate predictions; scaling effective ANE computation to massive graphs with millions of nodes pushes the difficulty of the problem to a whole new level. Existing solutions largely fail on such graphs, leading to prohibitive costs, low-quality embeddings, or both. This paper proposes PANE, an effective and scalable approach to ANE computation for massive graphs that achieves state-of-the-art result quality on multiple benchmark datasets, measured by the accuracy of three common prediction tasks: attribute inference, link prediction, and node classification. PANE obtains high scalability and effectiveness through three main algorithmic designs. First, it formulates the learning objective based on a novel random walk model for attributed networks. The resulting optimization task is still challenging on large graphs. Second, PANE includes a highly efficient solver for the above optimization problem, whose key module is a carefully designed initialization of the embeddings, which drastically reduces the number of iterations required to converge. Finally, PANE utilizes multi-core CPUs through non-trivial parallelization of the above solver, which achieves scalability while retaining the high quality of the resulting embeddings. Extensive experiments, comparing 10 existing approaches on 8 real datasets, demonstrate that PANE consistently outperforms all existing methods in terms of result quality, while being orders of magnitude faster.Comment: 16 pages. PVLDB 2021. Volume 14, Issue

Design Challenges of Intra- and Inter- Chiplet Interconnection

In a chiplet-based many-core system, intra- and inter- chiplet interconnection is key to system performance and power consumption. There are a few challenges in intra- and inter- chiplet interconnection network: 1) Fast and accurate simulation is necessary to analyze the performance metrics. 2) Efficient network architecture for inter- and intra- chiplet is necessary, including topology, PHY design and deadlock free routing algorithms, etc. 3) Deep learning based AI systems are demanding more computation power, which calls for the need of efficient and low power chiplet-based systems. This paper proposes network designs to address these challenges and provides future research directions

Identification of Close Relatives in the HUGO Pan-Asian SNP Database

The HUGO Pan-Asian SNP Consortium has recently released a genome-wide dataset, which consists of 1,719 DNA samples collected from 71 Asian populations. For studies of human population genetics such as genetic structure and migration history, this provided the most comprehensive large-scale survey of genetic variation to date in East and Southeast Asia. However, although considered in the analysis, close relatives were not clearly reported in the original paper. Here we performed a systematic analysis of genetic relationships among individuals from the Pan-Asian SNP (PASNP) database and identified 3 pairs of monozygotic twins or duplicate samples, 100 pairs of first-degree and 161 second-degree of relationships. Three standardized subsets with different levels of unrelated individuals were suggested here for future applications of the samples in most types of population-genetics studies (denoted by PASNP1716, PASNP1640 and PASNP1583 respectively) based on the relationships inferred in this study. In addition, we provided gender information for PASNP samples, which were not included in the original dataset, based on analysis of X chromosome data

Population Genetic Structure of Peninsular Malaysia Malay Sub-Ethnic Groups

Patterns of modern human population structure are helpful in understanding the history of human migration and admixture. We conducted a study on genetic structure of the Malay population in Malaysia, using 54,794 genome-wide single nucleotide polymorphism genotype data generated in four Malay sub-ethnic groups in peninsular Malaysia (Melayu Kelantan, Melayu Minang, Melayu Jawa and Melayu Bugis). To the best of our knowledge this is the first study conducted on these four Malay sub-ethnic groups and the analysis of genotype data of these four groups were compiled together with 11 other populations' genotype data from Indonesia, China, India, Africa and indigenous populations in Peninsular Malaysia obtained from the Pan-Asian SNP database. The phylogeny of populations showed that all of the four Malay sub-ethnic groups are separated into at least three different clusters. The Melayu Jawa, Melayu Bugis and Melayu Minang have a very close genetic relationship with Indonesian populations indicating a common ancestral history, while the Melayu Kelantan formed a distinct group on the tree indicating that they are genetically different from the other Malay sub-ethnic groups. We have detected genetic structuring among the Malay populations and this could possibly be accounted for by their different historical origins. Our results provide information of the genetic differentiation between these populations and a valuable insight into the origins of the Malay sub-ethnic groups in Peninsular Malaysia

NoC Frequency Scaling with Flexible-Pipeline Routers

Abstract—Voltage and frequency scaling (VFS) for NoC can potentially reduce energy consumption, but the associated increase in latency and degradation in throughput limits its deployment. We propose flexiblepipeline routers that reconfigure pipeline stages upon VFS, so that latency through such routers remains constant. With minimal hardware overhead, the deployment of such routers allows us to reduce network frequency and save network energy, without significant performance degradation. Furthermore, we demonstrate the use of simple performance metrics to determine the optimal operation frequency, considering the energy/performance impact on all aspects of the system- the cores, the caches and the interconnection network

Applying BIM and 3D laser scanning technology on virtual pre-assembly for complex steel structure in construction

Steel structure needs to be assembled before the components are transported to the site to ensure the installation of the steel structure successfully. The traditional assembly is a physical assembly method, which needs an amount of equipment, occupies large areas and can be labor and time-consuming. To address these issues, industrial photogrammetry has been developed to acquire data, and the steel structure has been virtually assembled in a virtual environment. The high-precision data acquisition of steel components is now carried out by using 3D laser scanner, and therefore, the feature data of those components can be automatically extracted by programming. As a result, the accurate linear shape of the post-splicing components can be extracted after completing the precise assembly of the steel structure in a virtual environment, which is significant for the subsequent mechanical analysis

Applying BIM and 3D laser scanning technology on virtual pre-assembly for complex steel structure in construction

Steel structure needs to be assembled before the components are transported to the site to ensure the installation of the steel structure successfully. The traditional assembly is a physical assembly method, which needs an amount of equipment, occupies large areas and can be labor and time-consuming. To address these issues, industrial photogrammetry has been developed to acquire data, and the steel structure has been virtually assembled in a virtual environment. The high-precision data acquisition of steel components is now carried out by using 3D laser scanner, and therefore, the feature data of those components can be automatically extracted by programming. As a result, the accurate linear shape of the post-splicing components can be extracted after completing the precise assembly of the steel structure in a virtual environment, which is significant for the subsequent mechanical analysis