11 citations · 22 across the 9 of their papers we have counts for
12 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…
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…
CSI-Free Symbol Detection for Atomic MIMO Receivers via In-Context Learning
Zihang Song, Qihao Peng, Pei Xiao +2
Atomic receivers based on Rydberg vapor cells as sensors of electromagnetic fields offer a promising alternative to conventional radio frequency front-ends. In multi-antenna config…
Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning
Jiechen Chen, Bipin Rajendran, Osvaldo Simeone
Neuromorphic and quantum computing have recently emerged as promising paradigms for advancing artificial intelligence, each offering complementary strengths. Neuromorphic systems b…
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…
Turbo-ICL: In-Context Learning-Based Turbo Equalization
Zihang Song, Matteo Zecchin, Bipin Rajendran +1
This paper introduces a novel in-context learning (ICL) framework, inspired by large language models (LLMs), for soft-input soft-output channel equalization in coded multiple-input…
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…
In-Context Learned Equalization in Cell-Free Massive MIMO via State-Space Models
Zihang Song, Matteo Zecchin, Bipin Rajendran +1
Sequence models have demonstrated the ability to perform tasks like channel equalization and symbol detection by automatically adapting to current channel conditions. This is done…
Neuromorphic In-Context Learning for Energy-Efficient MIMO Symbol Detection
Zihang Song, Osvaldo Simeone, Bipin Rajendran
In-context learning (ICL), a property demonstrated by transformer-based sequence models, refers to the automatic inference of an input-output mapping based on examples of the mappi…
Performance Evaluation of Neuromorphic Hardware for Onboard Satellite Communication Applications
Eva Lagunas, Flor Ortiz, Geoffrey Eappen +7
Spiking neural networks (SNNs) implemented on neuromorphic processors (NPs) can enhance the energy efficiency of deployments of artificial intelligence (AI) for specific workloads.…
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…