A Novel Reduction Circuit Based on Binary Tree Path Partition on FPGAs

Due to high parallelism, field-programmable gate arrays are widely used as accelerators in engineering and scientific fields, which involve a large number of operations of vector and matrix. High-performance accumulation circuits are the key to large-scale matrix operations. By selecting the adder as the reduction operator, the reduction circuit can implement the accumulation function. However, the pipelined adder will bring challenges to the design of the reduction circuit. To solve this problem, we propose a novel reduction circuit based on binary tree path partition, which can simultaneously handle multiple data sets with arbitrary lengths. It divides the input data into multiple groups and sends them to different iterations for calculation. The elements belonging to the same data set in each group are added to obtain a partial result, and the partial results of the same data set are added to achieve the final result. Compared with other reduction methods, it has the least area-time product.

Local Super Antimagic Total Labeling for Vertex Coloring of Graphs

Let G=(V,E) be a graph with vertex set V and edge set E. A local antimagic total vertex coloring f of a graph G with vertex-set V and edge-set E is an injective map from V∪E to {1,2,…,|V|+|E|} such that if for each uv∈E(G) then w(u)≠w(v), where w(u)=∑uv∈E(G)f(uv)+f(u). If the range set f satisfies f(V)={1,2,…,|V|}, then the labeling is said to be local super antimagic total labeling. This labeling generates a proper vertex coloring of the graph G with the color w(v) assigning the vertex v. The local super antimagic total chromatic number of graph G, χlsat(G) is defined as the least number of colors that are used for all colorings generated by the local super antimagic total labeling of G. In this paper we investigate the existence of the local super antimagic total chromatic number for some particular classes of graphs such as a tree, path, cycle, helm, wheel, gear, sun, and regular graphs as well as an amalgamation of stars and an amalgamation of wheels.

How to Combine Tree-Search Methods in Reinforcement Learning

Finite-horizon lookahead policies are abundantly used in Reinforcement Learning and demonstrate impressive empirical success. Usually, the lookahead policies are implemented with specific planning methods such as Monte Carlo Tree Search (e.g. in AlphaZero (Silver et al. 2017b)). Referring to the planning problem as tree search, a reasonable practice in these implementations is to back up the value only at the leaves while the information obtained at the root is not leveraged other than for updating the policy. Here, we question the potency of this approach. Namely, the latter procedure is non-contractive in general, and its convergence is not guaranteed. Our proposed enhancement is straightforward and simple: use the return from the optimal tree path to back up the values at the descendants of the root. This leads to a γh-contracting procedure, where γ is the discount factor and h is the tree depth. To establish our results, we first introduce a notion called multiple-step greedy consistency. We then provide convergence rates for two algorithmic instantiations of the above enhancement in the presence of noise injected to both the tree search stage and value estimation stage.

Absolute Mean Graceful Labeling in Path Union of Various Graphs

Present paper aims to focus on absolute mean graceful labeling in path union of various graphs. We proved path union of graphs like tree, path Pn, cycle Cn, complete bipartite graph Km, n, grid graph PM × Pn, step grid graph Stn and double step grid graph DStn are absolute mean graceful graphs.