… convolutional neural network accelerators, and proposes future research trends. We summarize
the mainstream CNN accelerators, … In the future, accelerators for training will become the …

… Deep convolutional neural networks (CNNs) have recently shown very high accuracy in a
… Given the high computational demands of CNNs, custom hardware accelerators are vital for …

… , executing them on accelerators designed for 2D CNNs … In this paper, we present a
survey of hardware accelerators … convolution and not those that perform only 2D convolution …

… reconfigurable accelerator for deep convolutional neural net… on eight-layer convolutional
neural network with leaky rectified … based on the convolution neural network with rank based …

… In this article, we propose a novel accelerator called WINONN. We fully discuss the chal…
Our high scalability design enables us to deploy sparse Winograd accelerators on very small …

… in convolutional neural network (CNN) models makes sparsity-aware CNN hardware designs
very attractive. The existing sparse CNN accelerators … sparse 2-D convolution, this paper …

… Edge TPUs are a domain of accelerators for low-power,edge … across 423K uniqueconvolutional
neural networks. Building upon … metrics ofEdge TPU accelerators. These learned models …

… 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 …

… convolutional neural network (CNN). In this Brief, we report an FPGA-based computation-efficient
reconfigurable CNN accelerator. It … that the proposed CNN accelerator achieves a peak …

… Plenty of existing accelerators are built for dense CNNs or … sparse accelerators on FPGAs,
the proposed accelerator can … Compared to prior dense accelerators, this accelerator can …