Phase control plays an important role in the precise synthesis of inorganic materials, as the phase structure has a profound influence on properties such as conductivity and chemical stability. Phase-controlled preparation has been challenging for the metallic-phase group-VI transition metal dichalcogenides (the transition metals are Mo and W, and the chalcogens are S, Se and Te), which show better performance in electrocatalysis than their semiconducting counterparts. Here, we report the large-scale preparation of micrometre-sized metallic-phase 1T'-MoX (X = S, Se)-layered bulk crystals in high purity. We reveal that 1T'-MoS crystals feature a distorted octahedral coordination structure and are convertible to 2H-MoS following thermal annealing or laser irradiation. Electrochemical measurements show that the basal plane of 1T'-MoS is much more active than that of 2H-MoS for the electrocatalytic hydrogen evolution reaction in an acidic medium.
Graph processing recently received intensive interests in light of a wide range of needs to understand relationships. It is well-known for the poor locality and high memory bandwidth requirement. In conventional architectures, they incur a significant amount of data movements and energy consumption which motivates several hardware graph processing accelerators. The current graph processing accelerators rely on memory access optimizations or placing computation logics close to memory. Distinct from all existing approaches, we leverage an emerging memory technology to accelerate graph processing with analog computation.This paper presents GRAPHR, the first ReRAM-based graph processing accelerator. GRAPHR follows the principle of near-data processing and explores the opportunity of performing massive parallel analog operations with low hardware and energy cost. The analog computation is suitable for graph processing because: 1) The algorithms are iterative and could inherently tolerate the imprecision; 2) Both probability calculation (e.g., PageRank and Collaborative Filtering) and typical graph algorithms involving integers (e.g., BFS/SSSP) are resilient to errors. The key insight of GRAPHR is that if a vertex program of a graph algorithm can be expressed in sparse matrix vector multiplication (SpMV), it can be efficiently performed by ReRAM crossbar. We show that this assumption is generally true for a large set of graph algorithms.GRAPHR is a novel accelerator architecture consisting of two components: memory ReRAM and graph engine (GE). The core graph computations are performed in sparse matrix format in GEs (ReRAM crossbars). The vector/matrix-based graph computation is not new, but ReRAM offers the unique opportunity to realize the massive parallelism with unprecedented energy efficiency and low hardware cost. With small subgraphs processed by GEs, the gain of performing parallel operations overshadows the wastes due to sparsity. The experiment results show that GRAPHR achieves a 16.01× (up to 132.67×) speedup and a 33.82× energy saving on geometric mean compared to a CPU baseline system. Compared to GPU, GRAPHR achieves 1.69× to 2.19× speedup and consumes 4.77× to 8.91× less energy. GRAPHR gains a speedup of 1.16× to 4.12×, and is 3.67× to 10.96× more energy efficiency compared to PIM-based architecture.
In this paper, we propose a general framework to study the tradeoff between energy efficiency (EE) and spectral efficiency (SE) in massive MIMO enabled HetNets while ensuring proportional rate fairness among users and taking into account the backhaul capacity constraint. We aim at jointly optimizing user association, spectrum allocation, power coordination, and the number of activated antennas, which is formulated as a multi-objective optimization problem maximizing EE and SE simultaneously. With the help of weighted Tchebycheff method, it is then transformed into a single-objective optimization problem, which is a mixed-integer non-convex problem and requires unaffordable computational complexity to find the optimum. Hence, a low-complexity effective algorithm is developed based on primal decomposition, where we solve the power coordination and number of antenna optimization problem and the user association and spectrum allocation problem separately. Both theoretical analysis and numerical results demonstrate that our proposed algorithm can fast converge within several iterations and significantly improve both the EE-SE tradeoff performance and rate fairness among users compared to other algorithms.
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