2 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
DLAS: An Exploration and Assessment of the Deep Learning Acceleration Stack
Perry Gibson, José Cano, Elliot J. Crowley +2
Deep Neural Networks (DNNs) are extremely computationally demanding, which presents a large barrier to their deployment on resource-constrained devices. Since such devices are wher…
Exploring Robustness of Image Recognition Models on Hardware Accelerators
Nikolaos Louloudakis, Perry Gibson, José Cano +1
As the usage of Artificial Intelligence (AI) on resource-intensive and safety-critical tasks increases, a variety of Machine Learning (ML) compilers have been developed, enabling c…
Bifrost: End-to-End Evaluation and Optimization of Reconfigurable DNN Accelerators
Axel Stjerngren, Perry Gibson, José Cano
Reconfigurable accelerators for deep neural networks (DNNs) promise to improve performance such as inference latency. STONNE is the first cycle-accurate simulator for reconfigurabl…
Optimizing Grouped Convolutions on Edge Devices
Perry Gibson, José Cano, Jack Turner +3
When deploying a deep neural network on constrained hardware, it is possible to replace the network's standard convolutions with grouped convolutions. This allows for substantial m…