3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration

Understand

The deep neural network (DNN) based AI applications on the edge require both low-cost computing platforms and high-quality services.

• However, the limited memory, computing resources, and power budget of the edge devices constrain the effectiveness of the DNN algorithms.
• Developing edge-oriented AI algorithms and implementations (e.g., accelerators) is challenging.
• In this paper, we summarize our recent efforts for efficient on-device AI development from three aspects, including both training and inference.