136 citations · 136 across the 3 of their papers we have counts for
3 papers
AxMoE: Characterizing the Impact of Approximate Multipliers on Mixture-of-Experts DNN Architectures
Omkar B Shende, Marcello Traiola, Gayathri Ananthanarayanan
Deep neural network (DNN) inference at the edge demands simultaneous improvements in accuracy, computational efficiency, and energy consumption. Approximate computing and Mixture-o…
PERTINENCE: Input-based Opportunistic Neural Network Dynamic Execution
Omkar Shende, Gayathri Ananthanarayanan, Marcello Traiola
Deep neural networks (DNNs) are widely used for their ability to model complex patterns across domains such as computer vision, speech recognition, and robotics. However, larger mo…
High-Throughput CNN Inference on Embedded ARM big.LITTLE Multi-Core Processors
Siqi Wang, Gayathri Ananthanarayanan, Yifan Zeng +3
IoT Edge intelligence requires Convolutional Neural Network (CNN) inference to take place in the edge devices itself. ARM big.LITTLE architecture is at the heart of prevalent comme…