Abhishek Moitra
Orcid: 0000-0002-0534-5206
According to our database1,
Abhishek Moitra
authored at least 32 papers
between 2018 and 2024.
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Bibliography
2024
IEEE Trans. Emerg. Top. Comput. Intell., April, 2024
ClipFormer: Key-Value Clipping of Transformers on Memristive Crossbars for Write Noise Mitigation.
CoRR, 2024
CoRR, 2024
2023
HyDe: A brid PCM/FeFET/SRAM vice-Search for Optimizing Area and Energy-Efficiencies in Analog IMC Platforms.
IEEE J. Emerg. Sel. Topics Circuits Syst., December, 2023
SpikeSim: An End-to-End Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., November, 2023
<i>XploreNAS</i>: Explore Adversarially Robust and Hardware-efficient Neural Architectures for Non-ideal Xbars.
ACM Trans. Embed. Comput. Syst., July, 2023
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., June, 2023
MCAIMem: a Mixed SRAM and eDRAM Cell for Area and Energy-efficient on-chip AI Memory.
CoRR, 2023
HyDe: A Hybrid PCM/FeFET/SRAM Device-search for Optimizing Area and Energy-efficiencies in Analog IMC Platforms.
CoRR, 2023
Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks.
CoRR, 2023
XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars.
CoRR, 2023
Hardware Accelerators for Spiking Neural Networks for Energy-Efficient Edge Computing.
Proceedings of the Great Lakes Symposium on VLSI 2023, 2023
Examining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing.
Proceedings of the Great Lakes Symposium on VLSI 2023, 2023
DeepCAM: A Fully CAM-based Inference Accelerator with Variable Hash Lengths for Energy-efficient Deep Neural Networks.
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2023
XPert: Peripheral Circuit & Neural Architecture Co-search for Area and Energy-efficient Xbar-based Computing.
Proceedings of the 60th ACM/IEEE Design Automation Conference, 2023
Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing.
Proceedings of the 60th ACM/IEEE Design Automation Conference, 2023
Proceedings of the 57th Asilomar Conference on Signals, Systems, and Computers, ACSSC 2023, Pacific Grove, CA, USA, October 29, 2023
2022
Noise Sensitivity-Based Energy Efficient and Robust Adversary Detection in Neural Networks.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2022
Examining the Robustness of Spiking Neural Networks on Non-ideal Memristive Crossbars.
Proceedings of the ISLPED '22: ACM/IEEE International Symposium on Low Power Electronics and Design, Boston, MA, USA, August 1, 2022
Rate Coding Or Direct Coding: Which One Is Better For Accurate, Robust, And Energy-Efficient Spiking Neural Networks?
Proceedings of the IEEE International Conference on Acoustics, 2022
MIME: adapting a single neural network for multi-task inference with memory-efficient dynamic pruning.
Proceedings of the DAC '22: 59th ACM/IEEE Design Automation Conference, San Francisco, California, USA, July 10, 2022
2021
DetectX - Adversarial Input Detection Using Current Signatures in Memristive XBar Arrays.
IEEE Trans. Circuits Syst. I Regul. Pap., 2021
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks.
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2021
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2021
2020
Circuits Syst. Signal Process., 2020
Exposing the Robustness and Vulnerability of Hybrid 8T-6T SRAM Memory Architectures to Adversarial Attacks in Deep Neural Networks.
CoRR, 2020
2018
Proceedings of the 2018 Conference on Design and Architectures for Signal and Image Processing, 2018