A Dynamic Adaptive Activation Neuron‐Transistor for Dynamic Sparse Neural Networks in Advanced Driving Assistance System

Abstract Neuromorphic computing provides a promising solution to the von Neumann bottleneck and has received a lot of research attention. However, due to the limitation of static threshold activation of traditional neuromorphic devices, it is difficult to simulate the dynamic sparsity characteristics of biological neuron, resulting in more than 90% computational redundancy in fully connected neural networks architectures based on classical devices, which has become a key issue for efficient neuromorphic computing. Here, a dynamically adaptive activation neuron‐transistor based on asymmetric electrodes and indium gallium zinc oxide thin films is proposed, overcoming the static activation limitations of conventional neuromorphic devices. The device achieves a dynamic adaptive activation similar to biometric neuron via gate voltage or UV irradiation, achieving a wide range of activation times (65 ms–13.5 s) and adjustable activation thresholds (2.5–7.7 V). Leveraging this device, a dynamic sparse spiking neural network (DS‐SNN) is constructed that enables in situ Hadamard‐based weight pruning/regeneration. Applied to autonomous driving object detection, the DS‐SNN achieves 85% accuracy with 42% sparsity, outperforming dense convolutional/spiking neural networks (320k /140k weights) while utilizing only ≈80k parameters. This hardware‐algorithm co‐design establishes a new paradigm for energy‐efficient edge‐computing electronics, exploring 3D integration of large‐scale neuromorphic processors.