activity
20212024
most citedADEPT: Automatic Differentiable DEsign of Photonic Tensor Cores

15 citations · 25 across the 11 of their papers we have counts for

collaborators

11 papers

stat.ME2024

A Powerful and Precise Feature-level Filter using Group Knockoffs

Jiaqi Gu, Zihuai He

Selecting important features that have substantial effects on the response with provable type-I error rate control is a fundamental concern in statistics, with wide-ranging practic…

cs.ET20242 cited

TeMPO: Efficient Time-Multiplexed Dynamic Photonic Tensor Core for Edge AI with Compact Slow-Light Electro-Optic Modulator

Meng Zhang, Dennis Yin, Nicholas Gangi +4

Electronic-photonic computing systems offer immense potential in energy-efficient artificial intelligence (AI) acceleration tasks due to the superior computing speed and efficiency…

quant-ph20241 cited

QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits

Tianlong Chen, Zhenyu Zhang, Hanrui Wang +6

Parameterized Quantum Circuits (PQC) have obtained increasing popularity thanks to their great potential for near-term Noisy Intermediate-Scale Quantum (NISQ) computers. Achieving…

stat.ME2023

Summary Statistics Knockoffs Inference with Family-wise Error Rate Control

Catherine Xinrui Yu, Jiaqi Gu, Zhaomeng Chen +1

Testing multiple hypotheses of conditional independence with provable error rate control is a fundamental problem with various applications. To infer conditional independence with…

physics.optics2023

Integrated multi-operand optical neurons for scalable and hardware-efficient deep learning

Chenghao Feng, Jiaqi Gu, Hanqing Zhu +7

The optical neural network (ONN) is a promising hardware platform for next-generation neuromorphic computing due to its high parallelism, low latency, and low energy consumption. H…

cs.ET20221 cited

Fuse and Mix: MACAM-Enabled Analog Activation for Energy-Efficient Neural Acceleration

Hanqing Zhu, Keren Zhu, Jiaqi Gu +4

Analog computing has been recognized as a promising low-power alternative to digital counterparts for neural network acceleration. However, conventional analog computing is mainly…