Abhishek Moitra

IEEE Trans. Very Large Scale Integr. Syst., November, 2024

Workload-Balanced Pruning for Sparse Spiking Neural Networks.

[BibT_eX]

[DOI]

IEEE Trans. Emerg. Top. Comput. Intell., August, 2024

RobustEdge: Low Power Adversarial Detection for Cloud-Edge Systems.

[BibT_eX]

[DOI]

IEEE Trans. Emerg. Top. Comput. Intell., April, 2024

Do we really need a large number of visual prompts?

[BibT_eX]

[DOI]

Neural Networks, 2024

When In-memory Computing Meets Spiking Neural Networks - A Perspective on Device-Circuit-System-and-Algorithm Co-design.

[BibT_eX]

[DOI]

Yuhang Li

CoRR, 2024

TReX- Reusing Vision Transformer's Attention for Efficient Xbar-based Computing.

[BibT_eX]

[DOI]

CoRR, 2024

ClipFormer: Key-Value Clipping of Transformers on Memristive Crossbars for Write Noise Mitigation.

[BibT_eX]

[DOI]

CoRR, 2024

Are SNNs Truly Energy-efficient? - A Hardware Perspective.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2024

HaLo-FL: Hardware-Aware Low-Precision Federated Learning.

[BibT_eX]

[DOI]

Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2024

TT-SNN: Tensor Train Decomposition for Efficient Spiking Neural Network Training.

[BibT_eX]

[DOI]

Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2024

PIVOT- Input-aware Path Selection for Energy-efficient ViT Inference.

[BibT_eX]

[DOI]

Proceedings of the 61st ACM/IEEE Design Automation Conference, 2024

MINT: Multiplier-less INTeger Quantization for Energy Efficient Spiking Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the 29th Asia and South Pacific Design Automation Conference, 2024

2023

HyDe: A brid PCM/FeFET/SRAM vice-Search for Optimizing Area and Energy-Efficiencies in Analog IMC Platforms.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

ACM Trans. Embed. Comput. Syst., July, 2023

SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networks.

[BibT_eX]

[DOI]

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., June, 2023

HyDe: A Hybrid PCM/FeFET/SRAM Device-search for Optimizing Area and Energy-efficiencies in Analog IMC Platforms.

[BibT_eX]

[DOI]

CoRR, 2023

Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2023

Do We Really Need a Large Number of Visual Prompts?

[BibT_eX]

[DOI]

CoRR, 2023

MINT: Multiplier-less Integer Quantization for Spiking Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2023

XploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars.

[BibT_eX]

[DOI]

CoRR, 2023

Hardware Accelerators for Spiking Neural Networks for Energy-Efficient Edge Computing.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Duy-Thanh Nguyen

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.

[BibT_eX]

[DOI]

Proceedings of the 60th ACM/IEEE Design Automation Conference, 2023

Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing.

[BibT_eX]

[DOI]

Proceedings of the 60th ACM/IEEE Design Automation Conference, 2023

Energy-efficient Hardware Design for Spiking Neural Networks (Extended Abstract).

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Rachel Sterneck

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2022

Adversarial Detection without Model Information.

[BibT_eX]

[DOI]

CoRR, 2022

Examining the Robustness of Spiking Neural Networks on Non-ideal Memristive Crossbars.

[BibT_eX]

[DOI]

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?

[BibT_eX]

[DOI]

Hyoungseob Park

Proceedings of the IEEE International Conference on Acoustics, 2022

MIME: adapting a single neural network for multi-task inference with memory-efficient dynamic pruning.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

IEEE Trans. Circuits Syst. I Regul. Pap., 2021

Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks.

[BibT_eX]

[DOI]

Karina Vasquez

Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2021

Efficiency-driven Hardware Optimization for Adversarially Robust Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2021

2020

A Novel and Efficient Hardware Accelerator Architecture for Signal Normalization.

[BibT_eX]

[DOI]

Circuits Syst. Signal Process., 2020

Exposing the Robustness and Vulnerability of Hybrid 8T-6T SRAM Memory Architectures to Adversarial Attacks in Deep Neural Networks.

[BibT_eX]

[DOI]