29 research outputs found
NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks
Deep Neural Networks (DNNs) have made significant improvements to reach the
desired accuracy to be employed in a wide variety of Machine Learning (ML)
applications. Recently the Google Brain's team demonstrated the ability of
Capsule Networks (CapsNets) to encode and learn spatial correlations between
different input features, thereby obtaining superior learning capabilities
compared to traditional (i.e., non-capsule based) DNNs. However, designing
CapsNets using conventional methods is a tedious job and incurs significant
training effort. Recent studies have shown that powerful methods to
automatically select the best/optimal DNN model configuration for a given set
of applications and a training dataset are based on the Neural Architecture
Search (NAS) algorithms. Moreover, due to their extreme computational and
memory requirements, DNNs are employed using the specialized hardware
accelerators in IoT-Edge/CPS devices. In this paper, we propose NASCaps, an
automated framework for the hardware-aware NAS of different types of DNNs,
covering both traditional convolutional DNNs and CapsNets. We study the
efficacy of deploying a multi-objective Genetic Algorithm (e.g., based on the
NSGA-II algorithm). The proposed framework can jointly optimize the network
accuracy and the corresponding hardware efficiency, expressed in terms of
energy, memory, and latency of a given hardware accelerator executing the DNN
inference. Besides supporting the traditional DNN layers, our framework is the
first to model and supports the specialized capsule layers and dynamic routing
in the NAS-flow. We evaluate our framework on different datasets, generating
different network configurations, and demonstrate the tradeoffs between the
different output metrics. We will open-source the complete framework and
configurations of the Pareto-optimal architectures at
https://github.com/ehw-fit/nascaps.Comment: To appear at the IEEE/ACM International Conference on Computer-Aided
Design (ICCAD '20), November 2-5, 2020, Virtual Event, US