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Found 12 papers, 6 papers with code
Fix-Con: Automatic Fault Localization and Repair of Deep Learning Model Conversions between Frameworks
Converting deep learning models between frameworks is a common step to maximize model compatibility across devices and leverage optimization features that may be exclusively provided in one deep learning framework.
DLAS: An Exploration and Assessment of the Deep Learning Acceleration Stack
Deep Neural Networks (DNNs) are extremely computationally demanding, which presents a large barrier to their deployment on resource-constrained devices.
Fault Localization for Buggy Deep Learning Framework Conversions in Image Recognition
To mitigate such errors, we present a novel approach towards fault localization and repair of buggy deep learning framework conversions, focusing on pre-trained image recognition models.
DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models
Owing to the increased use of image recognition tasks in safety-critical applications like autonomous driving and medical imaging, it is imperative to assess their robustness to changes in the computational environment, as the impact of parameters like deep learning frameworks, compiler optimizations, and hardware devices on model performance and correctness is not yet well understood.
MutateNN: Mutation Testing of Image Recognition Models Deployed on Hardware Accelerators
On top of that, AI methods such as Deep Neural Networks (DNNs) are utilized to perform demanding, resource-intensive and even safety-critical tasks, and in order to effectively increase the performance of the DNN models deployed, a variety of Machine Learning (ML) compilers have been developed, allowing compatibility of DNNs with a variety of hardware acceleration devices, such as GPUs and TPUs.
Exploring Effects of Computational Parameter Changes to Image Recognition Systems
On the other hand, model inference time was affected by all environment parameters with changes in hardware device having the most effect.
Productive Reproducible Workflows for DNNs: A Case Study for Industrial Defect Detection
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 significantly.
Bifrost: End-to-End Evaluation and Optimization of Reconfigurable DNN Accelerators
Reconfigurable accelerators for deep neural networks (DNNs) promise to improve performance such as inference latency.
Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation
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 hardware platform to improve its performance.
SECDA: Efficient Hardware/Software Co-Design of FPGA-based DNN Accelerators for Edge Inference
In this paper we propose SECDA, a new hardware/software co-design methodology to reduce design time of optimized DNN inference accelerators on edge devices with FPGAs.
Orpheus: A New Deep Learning Framework for Easy Deployment and Evaluation of Edge Inference
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 ubiquity of neural networks.
Optimizing Grouped Convolutions on Edge Devices
We observe that our new implementation scales well with the number of groups and provides the best inference times in all settings, improving the existing implementations of grouped convolutions in TVM, PyTorch and TensorFlow Lite by 3. 4x, 8x and 4x on average respectively.
