… It accelerates the learning speed by aggregating the policy at each system into a central policy
and ensures network adaptability by … both learning acceleration and network adaptability. …

… networks. We first explain how softwarized networks contribute to network adaptability …
function implementations from pure software to software implementations accelerated with …

… 3.3 Network Mapping Method For the proposed accelerator architecture, intuitively, we can
implement dedicated reconfigurable cores for each layer and realize an end-to-end mapping …

… and adaptable hardware accelerators for machine learning. … Neural networks based ML
algorithms give computers the … neural network accelerator-based architectures for Machine …

… On the other hand, the heterogeneity of demands and communication types and their
temporal dynamics require a high degree of adaptability in order to operate the network in an …

… with adaptable bitwidth, is a flexible solution to accommodate different types of networks and
… In fact, researchers have shown that networks can be compressed [22,23] and represented …

… a low-power DNN accelerator for mobile devices fabricated in … that leverages the adaptability
of neural networks to train … , a low-power DNN accelerator fabricated in 65 nm CMOS. …

… Federated learning aims to address the security and networking concerns by keeping the
datasets local at devices, whereas STOIC intelligently offloads jobs across multiple tiers of …

… In other words, our work also addresses adaptability to platforms and SLOs. … We pair each
tile with a sub-network size and the tile is taken as input to the sub-network of the size. All the …

… of a deep neural network after model training, we propose an accelerated edge cloud …
deep learning scheme. The incremental learning scheme can perform realtime learning by …