14 papers
Event-triggered Implicit Perturbation for Zeroth-Order Fine-Tuning of Spiking Transformers
Tengteng Lei, Prabodh Katti, Rashi Dutt +5
Zeroth-order (ZO) optimization estimates gradients using only forward-pass evaluations, making it suitable for fine-tuning non-differentiable, event-driven spiking neural networks…
Multi-quantum-channel mediated tunable single-photon skyrmions from metasurfaces
Yan Wang, Zhenyu Guo, Minggui Liang +3
Quantum optical skyrmions, as topologically robust quantum information carriers, hold transformative potential for resilient high-dimensional quantum information networks. However,…
Neuromorphic Diffusion Language Models: Addressing Compute and Memory Bottlenecks via Sparsity and Block Denoising
Dengyu Wu, Clement Ruah, Jiechen Chen +2
Autoregressive (AR) large language models (LLMs) are inherently inefficient at inference time because each generated token requires accessing the full set of model parameters, lead…
Neuromorphic Non-Orthogonal Multiple Access for Parallel Remote Inference via Vector Symbolic Architecture
Jiechen Chen, Zihang Song, Dengyu Wu +2
Emerging edge intelligence systems increasingly rely on dense deployments of always-on sensors that must convey task-relevant information to a remote model under tight energy and s…
Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning
Jiechen Chen, Bipin Rajendran, Osvaldo Simeone
The paper introduces a stochastic quantum spiking neuron that uses multi‑qubit circuits for internal quantum memory and enables event‑driven spike generation, and shows how network…
Neuromorphic Wireless Split Computing with Resonate-and-Fire Neurons
Dengyu Wu, Jiechen Chen, H. Vincent Poor +2
Neuromorphic computing offers an energy-efficient alternative to conventional deep learning accelerators, particularly for real-time processing of time-series data. However, many e…