Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

Compute-in-memory (CIM) is a new computing paradigm that addresses the memory-wall
problem in hardware accelerator design for deep learning. The input vector and weight …

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

Neuromorphic computing uses brain-inspired principles to design circuits that can perform
computational tasks with superior power efficiency to conventional computers. Approaches …

Recent advances in self-supervised learning and the Transformer architecture have
significantly improved natural language processing (NLP), achieving remarkably low …

The encoder-decoder framework is state-of-the-art for offline semantic image segmentation.
Since the rise in autonomous systems, real-time computation is increasingly desirable. In …

Quantizing the activation, weight, and gradient to 4-bit is promising to accelerate neural
network training. However, existing 4-bit training methods require custom numerical formats …

The past decade has seen the great potential of applying deep neural network (DNN) based
software to safety-critical scenarios, such as autonomous driving. Similar to traditional …