In recent times, the machine learning (ML) community has recognized the deep learning
(DL) computing model as the Gold Standard. DL has gradually become the most widely …

In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …

Federated learning (FL) is currently the most widely adopted framework for collaborative
training of (deep) machine learning models under privacy constraints. Albeit its popularity, it …

Machine learning is an established and frequently used technique in industry and
academia, but a standard process model to improve success and efficiency of machine …

Memristive crossbar architectures are evolving as powerful in-memory computing engines
for artificial neural networks. However, the limited number of non-volatile conductance states …

Communication constraints are one of the majorchallenges preventing the wide-spread
adoption of Federated Learning systems. Recently, Federated Distillation (FD), a new …

Large deep neural network (DNN) models pose the key challenge to energy efficiency due
to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or …

Joint task inference, which fully utilizes end edge cloud cooperation, can effectively enhance
the performance of deep neural network (DNN) inference services in the industrial internet of …

Encoding videos as neural networks is a recently proposed approach that allows new forms
of video processing. However, traditional techniques still outperform such neural video …

InIn HTTP Adaptive Streaming (HAS), each video is divided into smaller segments, and each
segment is encoded at multiple pre-defined bitrates to construct a bitrate ladder. To optimize …