Computation-efficient deep learning for computer vision: A survey
Over the past decade, deep learning models have exhibited considerable advancements,
reaching or even exceeding human-level performance in a range of visual perception tasks …
reaching or even exceeding human-level performance in a range of visual perception tasks …
MELTing point: Mobile Evaluation of Language Transformers
Transformers have revolutionized the machine learning landscape, gradually making their
way into everyday tasks and equipping our computers with``sparks of intelligence'' …
way into everyday tasks and equipping our computers with``sparks of intelligence'' …
The future of consumer edge-ai computing
In the last decade, deep learning has rapidly infiltrated the consumer end, mainly thanks to
hardware acceleration across devices. However, as we look toward the future, it is evident …
hardware acceleration across devices. However, as we look toward the future, it is evident …
[HTML][HTML] ColabNAS: Obtaining lightweight task-specific convolutional neural networks following Occam's razor
The current trend of applying transfer learning from convolutional neural networks (CNNs)
trained on large datasets can be an overkill when the target application is a custom and …
trained on large datasets can be an overkill when the target application is a custom and …
FPGA implementation of deep learning architecture for kidney cancer detection from histopathological images
Kidney cancer is the most common type of cancer, and designing an automated system to
accurately classify the cancer grade is of paramount importance for a better prognosis of the …
accurately classify the cancer grade is of paramount importance for a better prognosis of the …
On-device deep learning: survey on techniques improving energy efficiency of DNNs
A Boumendil, W Bechkit… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Providing high-quality predictions is no longer the sole goal for neural networks. As we live
in an increasingly interconnected world, these models need to match the constraints of …
in an increasingly interconnected world, these models need to match the constraints of …
DiMO-CNN: Deep Learning Toolkit-Accelerated Analytical Modeling and Optimization of CNN Hardware and Dataflow
The growing complexity of CNNs demands both hardware acceleration design and dataflow
mapping solutions. The large co-design solution space presents a huge challenge. We …
mapping solutions. The large co-design solution space presents a huge challenge. We …
OptimML: Joint Control of Inference Latency and Server Power Consumption for ML Performance Optimization
G Chen, X Wang - ACM Transactions on Autonomous and Adaptive …, 2024 - dl.acm.org
Power capping is an important technique for high-density servers to safely oversubscribe the
power infrastructure in a data center. However, power capping is commonly accomplished …
power infrastructure in a data center. However, power capping is commonly accomplished …
Unleashing Network/Accelerator Co-Exploration Potential on FPGAs: A Deeper Joint Search
Recently, algorithm-hardware co-exploration for neural networks (NNs) has become the key
to obtaining high-quality solutions. However, previous efforts for FPGAs focus on neural …
to obtaining high-quality solutions. However, previous efforts for FPGAs focus on neural …
Using the Abstract Computer Architecture Description Language to Model AI Hardware Accelerators
Artificial Intelligence (AI) has witnessed remarkable growth, particularly through the
proliferation of Deep Neural Networks (DNNs). These powerful models drive technological …
proliferation of Deep Neural Networks (DNNs). These powerful models drive technological …