4 papers
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…
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…
Neuromorphic Wireless Split Computing with Multi-Level Spikes
Dengyu Wu, Jiechen Chen, Bipin Rajendran +2
Inspired by biological processes, neuromorphic computing leverages spiking neural networks (SNNs) to perform inference tasks, offering significant efficiency gains for workloads in…