Deep neural networks (DNNs) are widely used in many fields, such as artificial intelligence
generated content (AIGC) and robotics. To efficiently support these tasks, the model pruning …

Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-
performance computing, playing pivotal roles in advancing fields like IoT, autonomous …

The deep learning models (DL) are becoming bigger, easily beyond the memory capacity of
a single accelerator. The recent progress in large DL training utilizes CPU memory as an …

Deep neural network (DNN) compression (eg, quantization, pruning) has been widely
investigated in variousdeep learning tasks (eg, vision and language). The development of …

Attention for transformers is a critical workload that has recently received significant
'attention'as a target for custom acceleration. Yet, while prior work succeeds in reducing …

Accelerating matrix multiplication is crucial to achieve high performance in many application
domains, including neural networks, graph analytics, and scientific computing. These …

Bit-level sparsity methods skip ineffectual zero-bit operations and are typically applicable
within bit-serial deep learning accelerators. This type of sparsity at the bit-level is especially …

The remarkable advancements in artificial intel-ligence (AI), primarily driven by deep neural
networks, are facing challenges surrounding unsustainable computational tra-jectories …

Benefiting from the self-attention mechanism, Transformer models have attained impressive
contextual comprehension capabilities for lengthy texts. The requirements of high …

Large language models (LLMs) have demonstrated remarkable performance across various
machine learning tasks. Yet the substantial memory footprint of LLMs significantly hinders …