Geoffrey W. Burr

Orcid: 0000-0001-5717-2549

According to our database1, Geoffrey W. Burr authored at least 34 papers between 1998 and 2023.

Collaborative distances:

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2023
A Heterogeneous and Programmable Compute-In-Memory Accelerator Architecture for Analog-AI Using Dense 2-D Mesh.
IEEE Trans. Very Large Scale Integr. Syst., 2023

An analog-AI chip for energy-efficient speech recognition and transcription.
Nat., 2023

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators.
CoRR, 2023

Phase Change Memory-based Hardware Accelerators for Deep Neural Networks (invited).
Proceedings of the 2023 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits), 2023

Architectures and Circuits for Analog-memory-based Hardware Accelerators for Deep Neural Networks (Invited).
Proceedings of the IEEE International Symposium on Circuits and Systems, 2023

Impact of Phase-Change Memory Drift on Energy Efficiency and Accuracy of Analog Compute-in-Memory Deep Learning Inference (Invited).
Proceedings of the IEEE International Reliability Physics Symposium, 2023

2022
Fair and Comprehensive Benchmarking of Machine Learning Processing Chips.
IEEE Des. Test, 2022

Analog-memory-based 14nm Hardware Accelerator for Dense Deep Neural Networks including Transformers.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2022

2021
Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices.
Frontiers Comput. Neurosci., 2021

Circuit Techniques for Efficient Acceleration of Deep Neural Network Inference with Analog-AI (Invited).
Proceedings of the IEEE International Symposium on Circuits and Systems, 2021


2020
Optimization of Analog Accelerators for Deep Neural Networks Inference.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2020

Neuromorphic Computing with Phase Change, Device Reliability, and Variability Challenges.
Proceedings of the 2020 IEEE International Reliability Physics Symposium, 2020

Accelerating Deep Neural Networks with Analog Memory Devices.
Proceedings of the 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems, 2020

2019
A role for analogue memory in AI hardware.
Nat. Mach. Intell., 2019

AI hardware acceleration with analog memory: Microarchitectures for low energy at high speed.
IBM J. Res. Dev., 2019

Analog-to-Digital Conversion With Reconfigurable Function Mapping for Neural Networks Activation Function Acceleration.
IEEE J. Emerg. Sel. Topics Circuits Syst., 2019

Non-filamentary non-volatile memory elements as synapses in neuromorphic systems.
Proceedings of the 19th Non-Volatile Memory Technology Symposium, 2019


2018
Equivalent-accuracy accelerated neural-network training using analogue memory.
Nat., 2018

2017
Toward on-chip acceleration of the backpropagation algorithm using nonvolatile memory.
IBM J. Res. Dev., 2017

Reducing circuit design complexity for neuromorphic machine learning systems based on Non-Volatile Memory arrays.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2017

Neuromorphic devices and architectures for next-generation cognitive computing.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2017

Improved Deep Neural Network Hardware-Accelerators Based on Non-Volatile-Memory: The Local Gains Technique.
Proceedings of the IEEE International Conference on Rebooting Computing, 2017

2016
Circuit-Level Benchmarking of Access Devices for Resistive Nonvolatile Memory Arrays.
IEEE J. Emerg. Sel. Topics Circuits Syst., 2016

Recent Progress in Phase-Change Memory Technology.
IEEE J. Emerg. Sel. Topics Circuits Syst., 2016

Accelerating machine learning with Non-Volatile Memory: Exploring device and circuit tradeoffs.
Proceedings of the IEEE International Conference on Rebooting Computing, 2016

Large-scale neural networks implemented with Non-Volatile Memory as the synaptic weight element: Impact of conductance response.
Proceedings of the 46th European Solid-State Device Research Conference, 2016

2015
Non-volatile memory as hardware synapse in neuromorphic computing: A first look at reliability issues.
Proceedings of the IEEE International Reliability Physics Symposium, 2015

2013
Nanoscale electronic synapses using phase change devices.
ACM J. Emerg. Technol. Comput. Syst., 2013

2008
Phase-change random access memory: A scalable technology.
IBM J. Res. Dev., 2008

Overview of candidate device technologies for storage-class memory.
IBM J. Res. Dev., 2008

2000
Holographic data storage technology.
IBM J. Res. Dev., 2000

1998
Holographic Data Storage.
Computer, 1998


  Loading...