… Consequently, a comparative study of different operating systems … This paper's center of
attention is the visual operating system … SoC is uniquely designed for embedded deep learning …

… The second generation of the Robot Operating System, ROS 2, was redesigned from the …
These characteristics shorten the learning curve for new engineers to apply what they know …

… these programs with evolutionary search and a learned cost model. To optimize the performance
of … We evaluate Ansor on both standard deep learning benchmarks and emerging new …

Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that
enables in-situ model training and testing on edge data. Despite having the same end goals …

Deep learning (DL) workloads include throughput-intensive training tasks and latency-sensitive
inference tasks. The dominant practice today is to provision dedicated GPU clusters for …

Existing deep neural network (DNN) frameworks optimize the computation graph of a DNN
by applying graph transformations manually designed by human experts. This approach …

Memristor crossbars are circuits capable of performing analog matrix-vector multiplications,
overcoming the fundamental energy efficiency limitations of digital logic. They have been …

… system in the era of artificial intelligence. PaddlePaddle, as the only fully-functioning open-source
deep learning … [Methods] In this paper, a brief history of the deep learning frameworks …

… The Android operating system has been the most popular for … In this paper, we propose
DL-Droid, a deep learning system … approach using the deep learning system. Our study reveals …

… In this research, we presented a machine learning based IDS method and to show general …
a Kali Linux operating system and three computers with a Windows 8 operating system. In the …