mobile networks will become programmable! migration paths to new technologies needed e.g., software radios create wireless physical layers dynamically we extend programmability to the signaling plane and MAC layer
Accelerating MATLAB with GPU Computing A Primer with Examples Acquiring Editor: Todd Green Editorial Project Manager: Lindsay Lawrence Project Manager: Mohana Natarajan Designer: Matthew Limbert Morgan Kaufmann is an imprint of Elsevier 225 Wyman St
Batched Sparse Matrix Multiplication for Accelerating Graph Convolutional Networks
对图卷积网络进行加速的批量稀疏矩阵乘法
作者的ppt的pdf版本Formulation of Graph Convolution
GraphConvolution( A, X w, bias)
Feature
forb← o to batchsize
do for ch←0 to channel
y:=2a a,w
Y=AXW
do
文章目录概相关工作主要内容代码
Accelerating Deep Learning by Focusing on the Biggest Losers
概
思想很简单, 在训练网络的时候, 每个样本都会产生一个损失L(f(xi),yi)\mathcal{L}(f(x_i),y_i)L(f(xi),yi), 训练的模式往往是批训练, 将一个批次∑iL(f(xi),yi)\sum_i \mathcal{L}(f(x_i),y_i)∑iL(f(xi),yi)所产生的损失的梯度都传回去,
Interaction of electromagnetic, acoustic, and even gravitational waves with accelerating bodies forms a class of nonstationary time-variant processes. Scattered waves contain intrinsic signatures of motion, which manifest in a broad range of phenomen
