论文标题
VSCNN：卷积神经网络加速器具有矢量稀疏性
VSCNN: Convolution Neural Network Accelerator With Vector Sparsity
论文作者
论文摘要
卷积神经网络（CNN）的硬件加速器可以实时应用人工智能技术。但是，大多数加速器仅支持密集的CNN计算或遭受复杂控制以支持细粒度稀疏网络。为了解决上述问题，本文提出了一个有效的CNN加速器，其中包含1-D向量广播的输入，以支持密集网络以及具有相同硬件和低开销的矢量稀疏网络。提出的设计在密集的CNN计算上实现了1.93倍的速度。
Hardware accelerator for convolution neural network (CNNs) enables real time applications of artificial intelligence technology. However, most of the accelerators only support dense CNN computations or suffers complex control to support fine grained sparse networks. To solve above problem, this paper presents an efficient CNN accelerator with 1-D vector broadcasted input to support both dense network as well as vector sparse network with the same hardware and low overhead. The presented design achieves 1.93X speedup over the dense CNN computations.
