We present a novel deep learning architecture in which the convolution
operation leverages heterogeneous kernels. The proposed HetConv (Heterogeneous
Kernel-Based Convolution) reduces the computation (FLOPs) and the number of
parameters as compared to standard convolution operation while still
maintaining representational efficiency. To show the effectiveness of our
proposed convolution, we present extensive experimental results on the standard
convolutional neural network (CNN) architectures such as VGG \cite{vgg2014very}
and ResNet \cite{resnet}. We find that after replacing the standard
convolutional filters in these architectures with our proposed HetConv filters,
we achieve 3X to 8X FLOPs based improvement in speed while still maintaining
(and sometimes improving) the accuracy. We also compare our proposed
convolutions with group/depth wise convolutions and show that it achieves more
FLOPs reduction with significantly higher accuracy.

通过使用异构卷积内核，我们提出了一种新颖的深度学习架构，在减少计算量和参数数量的同时保持代表性效率，实现了在 VGG 和 ResNet 等 CNN 架构中 3 到 8 倍 FLOPs 的速度提升，并与组 / 深度卷积进行比较，证明其具有更高的准确性和更多的 FLOPs 减少。

HetConv: 基于异构内核的卷积神经网络

HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs

Machine learning applications are increasingly deployed not only to serve
predictions using static models, but also as tightly-integrated components of
feedback loops involving dynamic, real-time decision making. These applications
pose a new set of requirements, none of which are difficult to achieve in
isolation, but the combination of which creates a challenge for existing
distributed execution frameworks: computation with millisecond latency at high
throughput, adaptive construction of arbitrary task graphs, and execution of
heterogeneous kernels over diverse sets of resources. We assert that a new
distributed execution framework is needed for such ML applications and propose
a candidate approach with a proof-of-concept architecture that achieves a 63x
performance improvement over a state-of-the-art execution framework for a
representative application.

针对机器学习在实时决策中的应用，提出需要一种新的分布式执行框架来实现毫秒延迟、高吞吐、任务图的自适应构建以及异构内核的执行等一系列要求，并提出了一个候选方案，其架构已经获得了 63 倍的性能提升。