Chang Gao

Orcid: 0000-0002-3284-4078

Affiliations:
  • University of Zurich and ETH Zurich, Zurich, Switzerland


According to our database1, Chang Gao authored at least 23 papers between 2017 and 2024.

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

Timeline

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Bibliography

2024
Spartus: A 9.4 TOp/s FPGA-Based LSTM Accelerator Exploiting Spatio-Temporal Sparsity.
IEEE Trans. Neural Networks Learn. Syst., January, 2024

Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer.
CoRR, 2024

Exploiting Symmetric Temporally Sparse BPTT for Efficient RNN Training.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
To Spike or Not to Spike: A Digital Hardware Perspective on Deep Learning Acceleration.
IEEE J. Emerg. Sel. Topics Circuits Syst., December, 2023

An Area-Efficient Ultra-Low-Power Time-Domain Feature Extractor for Edge Keyword Spotting.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2023

3ET: Efficient Event-based Eye Tracking using a Change-Based ConvLSTM Network.
Proceedings of the IEEE Biomedical Circuits and Systems Conference, 2023

FrameFire: Enabling Efficient Spiking Neural Network Inference for Video Segmentation.
Proceedings of the 5th IEEE International Conference on Artificial Intelligence Circuits and Systems, 2023

2022
Cerebron: A Reconfigurable Architecture for Spatiotemporal Sparse Spiking Neural Networks.
IEEE Trans. Very Large Scale Integr. Syst., 2022

Spiking Cochlea With System-Level Local Automatic Gain Control.
IEEE Trans. Circuits Syst. I Regul. Pap., 2022

Skydiver: A Spiking Neural Network Accelerator Exploiting Spatio-Temporal Workload Balance.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2022

A 23-μW Keyword Spotting IC With Ring-Oscillator-Based Time-Domain Feature Extraction.
IEEE J. Solid State Circuits, 2022

A 23μW Solar-Powered Keyword-Spotting ASIC with Ring-Oscillator-Based Time-Domain Feature Extraction.
Proceedings of the IEEE International Solid-State Circuits Conference, 2022

Intrinsic Sparse LSTM using Structured Targeted Dropout for Efficient Hardware Inference.
Proceedings of the 4th IEEE International Conference on Artificial Intelligence Circuits and Systems, 2022

Enabling Energy-Efficient Inference for Self-Attention Mechanisms in Neural Networks.
Proceedings of the 4th IEEE International Conference on Artificial Intelligence Circuits and Systems, 2022

2021
EILE: Efficient Incremental Learning on the Edge.
Proceedings of the 3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, 2021

2020
EdgeDRNN: Recurrent Neural Network Accelerator for Edge Inference.
IEEE J. Emerg. Sel. Topics Circuits Syst., 2020

Recurrent Neural Network Control of a Hybrid Dynamic Transfemoral Prosthesis with EdgeDRNN Accelerator.
CoRR, 2020

Recurrent Neural Network Control of a Hybrid Dynamical Transfemoral Prosthesis with EdgeDRNN Accelerator.
Proceedings of the 2020 IEEE International Conference on Robotics and Automation, 2020

EdgeDRNN: Enabling Low-latency Recurrent Neural Network Edge Inference.
Proceedings of the 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems, 2020

2019
Real-Time Speech Recognition for IoT Purpose using a Delta Recurrent Neural Network Accelerator.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2019

Live Demonstration: Real-Time Spoken Digit Recognition using the DeltaRNN Accelerator.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2019

2018
DeltaRNN: A Power-efficient Recurrent Neural Network Accelerator.
Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2018

2017
On-chip ID generation for multi-node implantable devices using SA-PUF.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2017


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