10 citations · 19 across the 10 of their papers we have counts for
4 papers · 1 filter
Runtime Deep Model Multiplexing for Reduced Latency and Energy Consumption Inference
Amir Erfan Eshratifar, Massoud Pedram
We propose a learning algorithm to design a light-weight neural multiplexer that given the input and computational resource requirements, calls the model that will consume the mini…
BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services
Amir Erfan Eshratifar, Amirhossein Esmaili, Massoud Pedram
Recent studies have shown the latency and energy consumption of deep neural networks can be significantly improved by splitting the network between the mobile device and cloud. Thi…
Towards Collaborative Intelligence Friendly Architectures for Deep Learning
Amir Erfan Eshratifar, Amirhossein Esmaili, Massoud Pedram
Modern mobile devices are equipped with high-performance hardware resources such as graphics processing units (GPUs), making the end-side intelligent services more feasible. Even r…
JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services
Amir Erfan Eshratifar, Mohammad Saeed Abrishami, Massoud Pedram
Deep learning models are being deployed in many mobile intelligent applications. End-side services, such as intelligent personal assistants, autonomous cars, and smart home service…