From the 1 of 10 linked papers with an AI index.
10 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…
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…
On-Device Fine-Tuning via Backprop-Free Zeroth-Order Optimization
Prabodh Katti, Houssem Sifaou, Sangwoo Park +2
On-device fine-tuning is a critical capability for edge AI systems, which must support adaptation to different agentic tasks under stringent memory constraints. Conventional backpr…
Towards Efficient and Reliable AI Through Neuromorphic Principles
Bipin Rajendran, Osvaldo Simeone, Bashir M. Al-Hashimi
Artificial intelligence (AI) research today is largely driven by ever-larger neural network models trained on graphics processing units (GPUs). This paradigm has yielded remarkable…