Yingyan Lin

CPT: Efficient Deep Neural Network Training via Cyclic Precision

2021 · arXiv (Cornell University)

Low-precision deep neural network (DNN) training has gained tremendous attention as reducing precision is one of the most effective knobs for boosting DNNs' training time/energy efficiency. In this paper, we attempt to explore low-precision training …

O-HAS: Optical Hardware Accelerator Search for Boosting Both Acceleration Performance and Development Speed

2021 · arXiv (Cornell University)

The recent breakthroughs and prohibitive complexities of Deep Neural Networks (DNNs) have excited extensive interest in domain-specific DNN accelerators, among which optical DNN accelerators are particularly promising thanks to their unprecedented potential of achieving superior …

GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models

2023 · arXiv (Cornell University)

The remarkable capabilities and intricate nature of Artificial Intelligence (AI) have dramatically escalated the imperative for specialized AI accelerators. Nonetheless, designing these accelerators for various AI workloads remains both labor- and time-intensive. While existing design …

Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI

2024 · arXiv (Cornell University)

The remarkable advancements in artificial intelligence (AI), primarily driven by deep neural networks, have significantly impacted various aspects of our lives. However, the current challenges surrounding unsustainable computational trajectories, limited robustness, and a lack of …