… accelerator for sparse CNNs on FPGAs. To efficiently deal with the irregular connections in
the sparse convolutional … Experiments demonstrate that our accelerator can achieve 223.4-…

… accelerator for convolutional neural networks. Our proposed … to implement modern
convolutional neural networks. We … into account, we this optical accelerator shows a 3 orders of …

Convolutional Neural Networks (CNN) are very computation-intensive. Recently, a lot of CNN
accelerators based on the CNN intrinsic parallelism are proposed. However, we observed …

Convolutional neural networks (CNNs) are emerging as powerful tools for image processing.
Recent machine learning work has reduced CNNs' compute and data volumes by …

… a CNN accelerator for … accelerator, called Eyeriss, that can support high throughput CNN
inference and optimizes for the energy efficiency of the entire system, including the accelerator …

… Abstract— Convolutional neural networks (CNNs) have become the dominant neural network
… We propose a flexible and efficient CNN accelerator architecture called NullHop that imple…

… convolutional neural network accelerator and we manage to recover the input image from
the collected power traces without knowing the detailed parameters in the neural network. For …

… (CNNs) are a type of deep neural network that achieve high accuracy for these tasks. CNNs
… accelerators. In this work we present Snowflake, a scalable, efficient low-power accelerator …

… of convolutional neural networks (CNNs) has achieved vast improvements by introducing
larger and deeper network … -limited accelerator designs, which are often restricted to store all …

… When a pruned network is run on an accelerator, the lack of … pruning scheme that reflects
accelerator architectures. In the … accelerator design for deep convolutional neural networks,” …