2 Cambricon-X: An Accelerator for Sparse Neural Networks 摘要《Cambricon-X:一种针对 AI芯片:寒武纪Cambricon-X结构分析 。 但是,删减了大量权值系数后,模型网络所需要的乘法计算次数明显变少,但是因为系数的稀疏带有不可控的随机性,不同filter的有效权重可能是不同位置的,所以,这就造成了大量权重并行计算时,...
Cambricon-X: an accelerator for sparse neural networks. In: Proceedings of the 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), 2016. 1--12. Google Scholar [16] Albericio J, Judd P, Hetherington T. Cnvlutin. SIGARCH Comput Archit News , 2016 , 44: 1 -13 ...
According to news reporting out of Chaoyang, People's\nRepublic of China, by NewsRx editors, research stated, "Various pruning skills and network compression\nmethods make modern neural networks sparse for both weights and activations. However, GPUs (graphics\nprocessing units) and most of the ...
sparse CNNField-programmable gate array (FPGA) has become an excellent hardware accelerator solution for convolutional neural networks (CNNs). Meanwhile, optimizing methods, such as model compression, have been proposed. As most CNN accelerators focus on dense neural networks, to solve the problem ...
We propose the ExTensor accelerator, which builds these novel ideas on handling sparsity into hardware to enable better bandwidth utilization and compute throughput. We evaluate ExTensor on several kernels relative to industry libraries (Intel MKL) and state-of-the-art tensor algebra compilers (TACO)...
Cambricon-X: An Accelerator for Sparse Neural Networks 摘要 神经网络(NNs)已被证明在广泛的应用中很有用,例如图像识别,自动翻译和广告推荐。由于不断增加的深层结构,即具有大量神经元和连接(即突触)的多层结构,因此,最新的NN既需要大量计算又需要大量内存。稀疏神经网络已成为减少所需计算量和内存量的有效解决方案...
An accelerator for sparse machine learning is used [382]. We cannot know where the zero values are in the vectors and matrices before the learning process because such values are randomly placed in the tensor as a learning result. In addition, there is a possibility of having an invalid acti...
An accelerator for sparse machine learning is used [382]. We cannot know where the zero values are in the vectors and matrices before the learning process because such values are randomly placed in the tensor as a learning result. In addition, there is a possibility of having an invalid acti...
论文阅读笔记PipeCNN: An OpenCL-Based Open-Source FPGA Accelerator for Convolution Neural Networks,程序员大本营,技术文章内容聚合第一站。
论文笔记:An Efficient Hardware Accelerator for Sparse Convolutional Neural Networks on FPGAs Abstract 深度卷积神经网络(CNN)以巨大的计算为代价取得了卓越的性能。随着CNN模型变得越来越复杂和深入,通过剪枝网络中的冗余连接来压缩CNN以使其稀疏已成为一种吸引人的方法,可以减少计算量和内存需求。另一方面,FPGA被证明...