4 papers
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…
SECDA: Efficient Hardware/Software Co-Design of FPGA-based DNN Accelerators for Edge Inference
Jude Haris, Perry Gibson, José Cano +2
Edge computing devices inherently face tight resource constraints, which is especially apparent when deploying Deep Neural Networks (DNN) with high memory and compute demands. FPGA…
Orpheus: A New Deep Learning Framework for Easy Deployment and Evaluation of Edge Inference
Perry Gibson, José Cano
Optimising deep learning inference across edge devices and optimisation targets such as inference time, memory footprint and power consumption is a key challenge due to the ubiquit…
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…