Track 10: Algorithms, Architecture, and Hardware Acceleration for AI on the Edge

Track 10 Chairs

Lan-Da Van National Chiao Tung University, Taiwan

Topics of interest

The topics of interest of the track include, but are not limited to:

  • Hardware-aware software optimizations and model compression
  • Hardware acceleration for machine learning
  • Synaptic plasticity and neuron motifs of learning dynamics
  • Specialized hardware architectures for energy-efficient Edge AI
  • Accelerator design and evaluation for ML/AI inference: Circuit and architecture design
  • Advanced techniques for energy-efficient inference and power management
  • Reliability, security, and robustness for Edge AI
  • Ultra-low-power memory system design for Edge AI
  • In-sensor processing, design, and implementation
  • Novel applications across all fields and emerging use cases of Edge AI
  • Distributed learning framework for IoT-Edge
  • Benchmark creation, assessment, and validation for EdgeAI
  • Fast design space exploration of ML/AI accelerators
  • In-sensor processing, design, and implementation