7 citations · 23 across the 13 of their papers we have counts for
14 papers
Exploiting Unstructured Sparsity in Fully Homomorphic Encrypted DNNs
Aidan Ferguson, Perry Gibson, Lara D'Agata +5
The deployment of deep neural networks (DNNs) in privacy-sensitive environments is constrained by computational overheads in fully homomorphic encryption (FHE). This paper explores…
AXI4MLIR: User-Driven Automatic Host Code Generation for Custom AXI-Based Accelerators
Nicolas Bohm Agostini, Jude Haris, Perry Gibson +6
This paper addresses the need for automatic and efficient generation of host driver code for arbitrary custom AXI-based accelerators targeting linear algebra algorithms, an importa…
FetaFix: Automatic Fault Localization and Repair of Deep Learning Model Conversions
Nikolaos Louloudakis, Perry Gibson, José Cano +1
Converting deep learning models between frameworks is a common step to maximize model compatibility across devices and leverage optimization features that may be exclusively provid…
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…
DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models
Nikolaos Louloudakis, Perry Gibson, José Cano +1
Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Fai…
Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition
Nikolaos Louloudakis, Perry Gibson, José Cano +1
When deploying Deep Neural Networks (DNNs), developers often convert models from one deep learning framework to another (e.g., TensorFlow to PyTorch). However, this process is erro…
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…
Exploring Effects of Computational Parameter Changes to Image Recognition Systems
Nikolaos Louloudakis, Perry Gibson, José Cano +1
Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and FPGAs for fast, timely processing. Fa…
Productive Reproducible Workflows for DNNs: A Case Study for Industrial Defect Detection
Perry Gibson, José Cano
As Deep Neural Networks (DNNs) have become an increasingly ubiquitous workload, the range of libraries and tooling available to aid in their development and deployment has grown si…
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…
Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation
Perry Gibson, José Cano
Auto-scheduling for tensor programs is a process where a search algorithm automatically explores candidate schedules (program transformations) for a given program on a target hardw…
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…