2 research outputs found
Cost-Driven Offloading for DNN-based Applications over Cloud, Edge and End Devices
Currently, deep neural networks (DNNs) have achieved a great success in
various applications. Traditional deployment for DNNs in the cloud may incur a
prohibitively serious delay in transferring input data from the end devices to
the cloud. To address this problem, the hybrid computing environments,
consisting of the cloud, edge and end devices, are adopted to offload DNN
layers by combining the larger layers (more amount of data) in the cloud and
the smaller layers (less amount of data) at the edge and end devices. A key
issue in hybrid computing environments is how to minimize the system cost while
accomplishing the offloaded layers with their deadline constraints. In this
paper, a self-adaptive discrete particle swarm optimization (PSO) algorithm
using the genetic algorithm (GA) operators was proposed to reduce the system
cost caused by data transmission and layer execution. This approach considers
the characteristics of DNNs partitioning and layers offloading over the cloud,
edge and end devices. The mutation operator and crossover operator of GA were
adopted to avert the premature convergence of PSO, which distinctly reduces the
system cost through enhanced population diversity of PSO. The proposed
offloading strategy is compared with benchmark solutions, and the results show
that our strategy can effectively reduce the cost of offloading for DNN-based
applications over the cloud, edge and end devices relative to the benchmarks
Compacting Deep Neural Networks for Internet of Things: Methods and Applications
Deep Neural Networks (DNNs) have shown great success in completing complex
tasks. However, DNNs inevitably bring high computational cost and storage
consumption due to the complexity of hierarchical structures, thereby hindering
their wide deployment in Internet-of-Things (IoT) devices, which have limited
computational capability and storage capacity. Therefore, it is a necessity to
investigate the technologies to compact DNNs. Despite tremendous advances in
compacting DNNs, few surveys summarize compacting-DNNs technologies, especially
for IoT applications. Hence, this paper presents a comprehensive study on
compacting-DNNs technologies. We categorize compacting-DNNs technologies into
three major types: 1) network model compression, 2) Knowledge Distillation
(KD), 3) modification of network structures. We also elaborate on the diversity
of these approaches and make side-by-side comparisons. Moreover, we discuss the
applications of compacted DNNs in various IoT applications and outline future
directions.Comment: 25 pages, 11 figure