9 citations · 42 across the 19 of their papers we have counts for
7 papers · 1 filter
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…
Improving Robustness Against Adversarial Attacks with Deeply Quantized Neural Networks
Ferheen Ayaz, Idris Zakariyya, José Cano +4
Reducing the memory footprint of Machine Learning (ML) models, particularly Deep Neural Networks (DNNs), is essential to enable their deployment into resource-constrained tiny devi…
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…
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…