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20212025
most citedDancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices

6 citations · 10 across the 5 of their papers we have counts for

collaborators

6 papers

quant-ph2025

Computational Performance Bounds Prediction in Quantum Computing with Unstable Noise

Jinyang Li, Samudra Dasgupta, Yuhong Song +3

Quantum computing has significantly advanced in recent years, boasting devices with hundreds of quantum bits (qubits), hinting at its potential quantum advantage over classical com…

quant-ph2025

Escaping Barren Plateau: Co-Exploration of Quantum Circuit Parameters and Architectures

Yipei Liu, Yuhong Song, Jinyang Li +4

Barren plateaus (BP), characterized by exponentially vanishing gradients that hinder the training of variational quantum circuits (VQC), present a pervasive and critical challenge…

quant-ph2025

QuSplit: Achieving Both High Fidelity and Throughput via Job Splitting on Noisy Quantum Computers

Jinyang Li, Yuhong Song, Yipei Liu +4

With the progression into the quantum utility era, computing is shifting toward quantum-centric architectures, where multiple quantum processors collaborate with classical computin…

quant-ph2024

Mera: Memory Reduction and Acceleration for Quantum Circuit Simulation via Redundancy Exploration

Yuhong Song, Edwin Hsing-Mean Sha, Longshan Xu +2

With the development of quantum computing, quantum processor demonstrates the potential supremacy in specific applications, such as Grovers database search and popular quantum neur…

cs.LG20214 cited

Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimization

Panjie Qi, Edwin Hsing-Mean Sha, Qingfeng Zhuge +5

State-of-the-art Transformer-based models, with gigantic parameters, are difficult to be accommodated on resource constrained embedded devices. Moreover, with the development of te…

cs.LG20216 cited

Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices

Yuhong Song, Weiwen Jiang, Bingbing Li +6

A pruning-based AutoML framework for run-time reconfigurability, namely RT3, is proposed in this work. This enables Transformer-based large Natural Language Processing (NLP) models…