8 citations · 14 across the 13 of their papers we have counts for
16 papers
Standard-Compliant Neuromorphic Integrated Sensing and Communications Aided by an Intelligent Reflecting Surface
Jiho Park, Jiechen Chen, Joonhyuk Kang +1
Neuromorphic computing enables event-driven, low-power inference and is therefore an attractive technology for jointly carrying out communication, sensing, and processing at resour…
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…
From High-Level Requirements to KPIs: Conformal Signal Temporal Logic Learning for Wireless Communications
Jiechen Chen, Michele Polese, Osvaldo Simeone
Softwarized radio access networks (RANs), such as those based on the Open RAN (O-RAN) architecture, generate rich streams of key performance indicators (KPIs) that can be leveraged…
Communicating Properties of Quantum States over Classical Noisy Channels
Nikhitha Nunavath, Jiechen Chen, Osvaldo Simeone +2
Transmitting information about quantum states over classical noisy channels is an important problem with applications to science, computing, and sensing. This task, however, poses…
Bayesian Optimization for Non-Cooperative Game-Based Radio Resource Management
Yunchuan Zhang, Jiechen Chen, Junshuo Liu +1
Radio resource management in modern cellular networks often calls for the optimization of complex utility functions that are potentially conflicting between different base stations…
Online Reliable Anomaly Detection via Neuromorphic Sensing and Communications
Junya Shiraishi, Jiechen Chen, Osvaldo Simeone +1
This paper proposes a low-power online anomaly detection framework based on neuromorphic wireless sensor networks, encompassing possible use cases such as brain-machine interfaces…
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…
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…
Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning
Jiechen Chen, Sangwoo Park, Petar Popovski +2
Neuromorphic computing leverages the sparsity of temporal data to reduce processing energy by activating a small subset of neurons and synapses at each time step. When deployed for…
Agreeing to Stop: Reliable Latency-Adaptive Decision Making via Ensembles of Spiking Neural Networks
Jiechen Chen, Sangwoo Park, Osvaldo Simeone
Spiking neural networks (SNNs) are recurrent models that can leverage sparsity in input time series to efficiently carry out tasks such as classification. Additional efficiency gai…
Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guarantees
Jiechen Chen, Sangwoo Park, Osvaldo Simeone
Spiking neural networks (SNNs) process time-series data via internal event-driven neural dynamics. The energy consumption of an SNN depends on the number of spikes exchanged betwee…
Neuromorphic Integrated Sensing and Communications
Jiechen Chen, Nicolas Skatchkovsky, Osvaldo Simeone
Neuromorphic computing is an emerging technology that support event-driven data processing for applications requiring efficient online inference and/or control. Recent work has int…
Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference
Jiechen Chen, Nicolas Skatchkovsky, Osvaldo Simeone
Neuromorphic computing is an emerging computing paradigm that moves away from batched processing towards the online, event-driven, processing of streaming data. Neuromorphic chips,…
Joint Resource Allocation and Cache Placement for Location-Aware Multi-User Mobile Edge Computing
Jiechen Chen, Hong Xing, Xiaohui Lin +2
With the growing demand for latency-critical and computation-intensive Internet of Things (IoT) services, the IoT-oriented network architecture, mobile edge computing (MEC), has em…