Silicon-photonics-based optical neural network (ONN) is a promising hardware platform that
could represent a paradigm shift in efficient AI with its CMOS-compatibility, flexibility, ultra …

Convolutional neural networks (CNNs)(including 2D and 3D convolutions) are popular in
video analysis tasks such as action recognition and activity understanding. Fast algorithms …

Approximate nearest neighbor search (ANNS) is a key retrieval technique for vector
database and many data center applications, such as person re-identification and …

Fine-grained sparsity in recent bio-inspired models such as attention-based model could
reduce the computation complexity dramatically. However, the unique sparsity pattern …

Deep neural networks (DNN) have shown superior performance in a variety of tasks. As they
rapidly evolve, their escalating computation and memory demands make it challenging to …

Resistive Random-Access Memory (RRAM) is well-suited to accelerate neural network (NN)
workloads as RRAM-based Processing-in-Memory (PIM) architectures natively support …

The relentless advancement of deep learning applications, particularly the highly potent yet
computationally intensive deep unsupervised learning models, is pushing the boundaries of …

Deep learning and graph processing are two big-data applications and they are widely
applied in many domains. The training of deep learning is essential for inference and has …

Deep learning is the core of artificial intelligence and it achieves state-of-the-art in a wide
range of applications. The intensity of computation and data in deep learning processing …