Our paper, “Hardware-Aware Early Termination for Low-Latency Spiking Neural Network Inference,” has been accepted for publication in IEEE Access. Abstract: Spiking Neural Networks (SNNs) provide a natural computation model for neuromorphic hardware, but fixed-timestep inference can execute substantial redundant temporal computation. This work proposes a hardware-aware early termination (ET) framework that determines the stopping time…

Our paper, “SuperMETER: An Efficient Superpixel-Based Architecture for Monocular Depth Estimation on Resource-Constrained IoT Platforms,” has been accepted for publication in the IEEE Internet of Things Journal.

Our new laboratory website has been launched.