11 citations · 34 across the 15 of their papers we have counts for
19 papers
TerMeZO: Ternary Sparse Zeroth-Order Optimization for Fine-tuning BitNet Models at the Edge
Houssem Sifaou, Prabodh Katti, Bipin Rajendran +1
Fine-tuning anguage models (LLMs) with first-order optimizers requires a memory several times larger than that required for inference. Memory-efficient zeroth-order optimization (M…
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…
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…
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…
Conformal Sparsification for Bandwidth-Efficient Edge-Cloud Speculative Decoding
Payel Bhattacharjee, Fengwei Tian, Meiyu Zhong +3
Edge-cloud speculative decoding (SD) accelerates inference by having a cloud-based large language model (LLM) that verifies draft tokens generated by a resource-constrained small l…
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…
Context-Aware Doubly-Robust Semi-Supervised Learning
Clement Ruah, Houssem Sifaou, Osvaldo Simeone +1
The widespread adoption of artificial intelligence (AI) in next-generation communication systems is challenged by the heterogeneity of traffic and network conditions, which call fo…
Bayes2IMC: In-Memory Computing for Bayesian Binary Neural Networks
Prabodh Katti, Clement Ruah, Osvaldo Simeone +2
Bayesian Neural Networks (BNNs) provide superior estimates of uncertainty by generating an ensemble of predictive distributions. However, inference via ensembling is resource-inten…
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…
Xpikeformer: Hybrid Analog-Digital Hardware Acceleration for Spiking Transformers
Zihang Song, Prabodh Katti, Osvaldo Simeone +1
The integration of neuromorphic computing and transformers through spiking neural networks (SNNs) offers a promising path to energy-efficient sequence modeling, with the potential…
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…
Stochastic Spiking Attention: Accelerating Attention with Stochastic Computing in Spiking Networks
Zihang Song, Prabodh Katti, Osvaldo Simeone +1
Spiking Neural Networks (SNNs) have been recently integrated into Transformer architectures due to their potential to reduce computational demands and to improve power efficiency.…
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.…