Lita Yang

Orcid: 0000-0001-6684-7069

According to our database1, Lita Yang authored at least 11 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

On csauthors.net:

Bibliography

2024
11.2 A 3D integrated Prototype System-on-Chip for Augmented Reality Applications Using Face-to-Face Wafer Bonded 7nm Logic at <2μm Pitch with up to 40% Energy Reduction at Iso-Area Footprint.
Proceedings of the IEEE International Solid-State Circuits Conference, 2024

2022
Three-Dimensional Stacked Neural Network Accelerator Architectures for AR/VR Applications.
IEEE Micro, 2022

2020
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019
An Always-On 3.8 $\mu$ J/86% CIFAR-10 Mixed-Signal Binary CNN Processor With All Memory on Chip in 28-nm CMOS.
IEEE J. Solid State Circuits, 2019

2018
An always-on 3.8μJ/86% CIFAR-10 mixed-signal binary CNN processor with all memory on chip in 28nm CMOS.
Proceedings of the 2018 IEEE International Solid-State Circuits Conference, 2018

Bit Error Tolerance of a CIFAR-10 Binarized Convolutional Neural Network Processor.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2018

TRIG: hardware accelerator for inference-based applications and experimental demonstration using carbon nanotube FETs.
Proceedings of the 55th Annual Design Automation Conference, 2018

BinarEye: An always-on energy-accuracy-scalable binary CNN processor with all memory on chip in 28nm CMOS.
Proceedings of the 2018 IEEE Custom Integrated Circuits Conference, 2018

2017
Approximate SRAM for Energy-Efficient, Privacy-Preserving Convolutional Neural Networks.
Proceedings of the 2017 IEEE Computer Society Annual Symposium on VLSI, 2017

SRAM voltage scaling for energy-efficient convolutional neural networks.
Proceedings of the 18th International Symposium on Quality Electronic Design, 2017

2015
Mixed-signal circuits for embedded machine-learning applications.
Proceedings of the 49th Asilomar Conference on Signals, Systems and Computers, 2015


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