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With the ever-increasing hardware design complexity comes the realization that efforts required for hardware verification increase at an even faster rate. Driven by the push from the desired verification productivity boost and the pull from leap-ahead capabilities of machine learning (ML), recent years have witnessed the emergence of exploiting ML-based techniques to improve the efficiency of hardware verification. In this article, we present a panoramic view of how ML-based techniques are embraced in hardware design verification, from formal verification to simulation-based verification, from academia to industry, and from current progress to future prospects. We envision that the adoption of ML-based techniques will pave the road for more scalable, more intelligent, and more productive hardware verification. 

High-Sr/Y granitoids in continental settings are sometimes erroneously regarded as the products derived from partial melting of thickened/delaminated mafic lower curst under relatively higher pressures (1.5 GPa) in a collisional orogenic setting. In fact, multiple magmatic processes in the trans-crustal magma system, such as recycling of antecrysts, crustal assimilation, and fractional crystallization, can create or modify the primary “adakitic” signature. As a result, the generation of adakitic magmas in continental settings remains controversial from a bulk-rock perspective. Here, we address the origin of adakitic plutonic rocks through geochemical and textural characterization of rock-forming minerals in the pyroxene-bearing Zhuyuan granodiorite, West Qinling, China. The Zhuyuan granodiorite formed in a post-collisional setting and primarily consists of resorbed orthopyroxene, three types of clinopyroxene, amphibole, two types of plagioclases, K-feldspar, biotite, and quartz. Type-1 Cpx has high XMg (70.0–81.7). Type-2 Cpx displays normal zoning and decreasing XMg (80.9 to 71.5) from the core to rim. Type-3 Cpx is reversely zoned, where the rims have higher XMg (75.5–86.9), Ni, Cr, suggesting a recharge event. Orthopyroxene has high-Ni and -Cr contents, as well as high XMg (80.9–82.8), indicative of antecrysts that grew in mafic magma reservoirs. The injection of magmas from different sources is supported by sieve-textured plagioclase and crystal size distributions of non-poikilitic amphibole. Finally, non-sieve textured plagioclase, biotite, K-feldspar, and quartz are late-crystallized phases, indicative of an orthocrystic origin. The melts in equilibrium with these orthocrysts display significantly higher Sr/Y values than the magma batches that crystallized other mafic phases (i.e., amphibole, clinopyroxene, and orthopyroxene). Thus, we propose that the system involved an initial high-Sr/Y melts in equilibrium with the orthocryst assemblage was generated by water-fluxed melting of intermediate to felsic sources. The addition of low Sr/Y non-orthocrysts (e.g., amphibole and pyroxene) and associated melt diluted the original “adakitic signal” in the magma reservoir and drove the bulk composition to more mafic values. Consequently, the Zhuyuan pyroxene-bearing granodiorite represents a mixture of crystals with diverse origins and distinct magma batches of various compositions (from felsic to mafic compositions). Our study emphasizes that the origin of adakitic granitoids cannot be clearly deciphered without geochemical analysis of the constituent minerals. We also suggest that Sr/Y values in plutons should be cautiously used in paleo-crustal thickness estimates in collisional settings because of possible open system scenarios as described here. 

Many relational data in our daily life are represented as graphs, making graph application an important workload. Because of the large scale of graph datasets, moving graph data to the cloud becomes a popular option. To keep the confidential and private graph secure from an untrusted cloud server, many cryptographic techniques are leveraged to hide the content of the data. However, protecting only the data content is not enough for a graph database. Because the structural information of the graph can be revealed through the database accessing track. In this work, we study the graph neural network (GNN), an important graph workload to mine information from a graph database. We find that the server is able to infer which node is processing during the edge retrieving phase and also learn its neighbor indices during GNN's aggregation phase. This leads to the leakage of the information of graph structure data. In this work, we present SPG, a structure-private graph database with SqueezePIR. Our SPG is built on top of Private Information Retrieval (PIR), which securely hides which nodes/neighbors are accessed. In addition, we propose SqueezePIR, a compression technique to overcome the computation overhead of PIR. Based on our evaluation, our SqueezePIR achieves 11.85× speedup on average with less than 2% accuracy loss when compared to the state-of-the-art FastPIR protocol.