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Recent Publications

Machine Learning

  1. [ICCAD 2023] D.Chen, Y. Zhang, S. Kundu, C. Li, P. A. Beerel. “RNA-ViT: Reduced-Dimension Approximate Normalized Attention Vision Transformers for Latency Efficient Private Inference” (accepted)
  2. [ICCV 2023] Y. Zhang*, D. Chen*, S. Kundu*, C. Li, P. A. Beerel.  “SAL-ViT: Towards Latency Efficient Private Inference on ViT using Selective Attention Search with a Learnable Softmax Approximation” (accepted)
  3. [DAC 2023] Y. Zhang, D. Chen, S. Kundu, H. Liu, R. Peng, P. A. Beerel. “C2PI: An Efficient Crypto-Clear Two-Party Neural Network Private Inference” (accepted)
  4. [ISPLED 2023] G. Datta, H. Deng, R. Aviles, Z. Liu, P. A. Beerel. “Bridging the Gap between Spiking Neural Networks & LSTMs for Latency & Energy Efficiency. (accepted)[
  5. [CVPR Workshop on Efficient Deep Learning for Computer Vision 2023] S. Kundu, Y. Zhang, D. Chen, P. A. Beerel. “Making Models Shallow Again: Jointly Learning to Reduce Non-Linearity and Depth for Latency-Efficient Private Inference” (oral presentation)
  7. [Frontiers in Neuroinformatics 2023] M. Kaiser, G. Datta, Z. Wang, A. Jacob, P. A Beerel, A. Jaiswal. “Neuromorphic-P2M: Processing-in-Pixel-in-Memory Paradigm for Neuromorphic Image Sensors” (accepted)
  8. [ICLR 2023] S. Kundu, J. Liu; S. Lu; P. A. Beerel. “Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference”
  9. [WACV 2023] G. Datta, Z. Liu, Z. Lin, A. Jaiswal, and P. A. Beerel. “Enabling ISP-less Low-Power Computer Vision”
  10. [WACV 2023] F. Chen*, G. Datta*, S. Kundu, and P. A. Beerel. “Self-Attentive Pooling for Efficient Deep Learning”
  11. [WACV 2023] S. Kundu, S. Sundaresan, M. Pedram, P. A. Beerel. “FLOAT: Fast Learnable Once-for-All Adversarial Training for Tunable Trade-off between Accuracy and Robustness”
  12. [ICASSP 2023] S. Kundu, S. Sundaresan, S. N. Sridhar, S. Lu, H. Tang, P. A. Beerel “Sparse mixture once-for-all adversarial training for efficient in-situ trade-off between accuracy and robustness of DNNs”
  13. [ICASSP 2023] H. Wang, C. Imes, S. Kundu, P. A. Beerel, S. P. Crago, J.P. Walters. “Quantpipe: Applying Adaptive Post-Training Quantization For Distributed Transformer Pipelines In Dynamic Edge Environments”
  14. [ICASSP 2023] G. Datta, Z. Liu, M. Kaiser, S. Kundu, J. Mathai, Z. Yin, A. P. Jacob, A. R. Jaiswal, P. A. Beerel. ‘In-Sensor & Neuromorphic Computing Are all You Need for Energy Efficient Computer Vision”
  15. [VLSI-SoC 2022] G. Datta*, S. Kundu*, Z. Yin*, J. Mathai, Z. Liu, Z. Wang, M. Tian, S. Lu, R. T. Lakkireddy, A. Schmidt, W. Abd-Almageed, A. P. Jacob, A. Jaiswal, P. A Beerel. “P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking” [Nominated for the Best Paper Award]
  16. [ECCV Workshop on Distributed Smart Cameras 2022] G. Datta, Z. Yin, A. P. Jacob, A. Jaiswal, P. A Beerel. “Towards Energy-Efficient Hyperspectral Image Processing inside Camera Pixels”
  17. [Nature Scientific Reports 2022] G. Datta*, S. Kundu*, Z Yin*, RT Lakkireddy, PA Beerel, A Jacob, AR Jaiswal. “P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications“
  18. [Frontiers Neuroscience] G. Datta, S. Kundu, A. Jaiswal, P. A Beerel. “ACE-SNN: Algorithm-Hardware Co-Design of Energy-efficient & Low-Latency Deep Spiking Neural Networks for 3D Image Recognition” (accepted)
  19. [IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2022] G. Datta, T. Etchartt, V. Yadav, V. Hedau, P. Natarajan, S. Chang. “ASD-Transformer: Efficient Active Speaker Detection using Self and Multimodal Transformers”,
  20. [ACM Trans. on Embedded Computing Systems 2022] S. Kundu, Y. Fu, B. Ye, P. A. Beerel, M. Pedram. “Towards Adversary Aware Non-Iterative Model Pruning Through Dynamic Network Rewiring of DNNs”,
  21. [DATE 2022] G. Datta, P. A. Beerel, “Can Deep Neural Networks be Converted to Ultra Low-Latency Spiking Neural Networks?”.
  22. [DATE 2022] S. Kundu, S. Wang, Q. Sun, P. A. Beerel, M. Pedram, “BMPQ: Bit-Gradient Sensitivity-Driven Mixed-Precision Quantization of DNNs from Scratch”.
  23. [NeurIPS 2021] S. Kundu, Q. Sun, Y. Fu, M. Pedram, P. A. Beerel, “Analyzing the Confidentiality of Undistillable Teachers in Knowledge Distillation” (initial work accepted at CVPR Workshop 2021).
  24. [ICCV 2021] S. Kundu, M. Pedram, P. A. Beerel, “HIRE-SNN: Harnessing the Inherent Robustness of Deep Spiking Neural Networks by Training with Crafted Input Noise”.
  25. [IJCNN 2021] G. Datta, S. Kundu, P. A. Beerel. “Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding”
  26. [ICASSP 2021] S. Kundu, S. Sundaresan, “AttentionLite: Towards Efficient Self-Attention Models for Vision”.
  27. [WACV 2021 S. Kundu, G.Datta, M. Pedram, P. A. Beerel, “ Spike-Thrift: Towards Energy-Efficient Deep Spiking Neural Networks by Limiting Spiking Activity via Attention-Guided Compression”.
  28. [ASP-DAC 2021] S. Kundu, M. Nazemi, P. A. Beerel, M. Pedram, “DNR: A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNs>”.
  29. [IEEE Trans. on Computers 2020] S. Kundu, M. Nazemi, M. Pedram, K. M. Chugg, P. A. Beerel, “Pre-defined Sparsity for Low-Complexity Convolutional Neural Networks”.
  30. [IEEE Journal on Emerging and Selected Topics in Circuits and Systems 2019] S. Dey, K.-W. Huang, P. A Beerel, K. M Chugg, “Pre-defined Sparsity for Low-Complexity Convolutional Neural Networks
  31. [Allerton 2019] S. Kundu*, S. Prakash*, H. Akrami, P. A. Beerel, K. M. Chugg, “pSConv: A Pre-defined Sparse Kernel Based Convolution for Deep CNNs”.
  32. [ISVLSI 2019] S. Kundu*, A. Fayyazi*, Shahin Nazarian, Peter A. Beerel, Massoud Pedram, “CSrram: Area-Efficient Low-Power Ex-Situ Training Framework for Memristive Neuromorphic Circuits Based on Clustered Sparsity”.

Arxiv Preprints:

  1. S. Kundu, G. Datta, M. Pedram, P. A. Beerel, “Towards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compression”, 2021.
  2. S. Kundu, H. Mostafa, S. Sridhar, S. Sundaresan, “Attention-based Image Upsampling”, 2020.

Hardware Security

  1. [GLSVLSI 2023] D. Chen, C. Goins, M. Waugaman, G. D. Dimou, P. A. Beerel “Island-based Random Dynamic Voltage Scaling vs ML-Enhanced Power Side-Channel Attacks” (invited paper)
  2. [IEEE Trans on CAD] Hu, Y. Zhang, K. Yang, D. Chen, P. A. Beerel, and P. Nuzzo. “On the Security of Sequential Logic Locking Against Oracle-Guided Attacks,” accepted, Feb 2023.
  3. [ISQED 2023] D.Chen, X. Zhou, Y. Hu, Y. Zhang, K. Yang, A. Rittenbach, P.  Nuzzo and P. A. Beerel. “Unraveling Latch Locking Using Machine Learning, Boolean Analysis, and ILP.”
  4. [CHES 2022] P. A. Beerel, M. Georgiou, B. Hamlin, A. J. Malozemoff, P. Nuzzo. “Towards a Formal Treatment of Logic Locking”, Conference on Cryptographic Hardware and Embedded Systems”.
  5. [DATE 2022] Y. Zhang*, Y. Hu*, P. Nuzzo and P. A. Beerel , “TriLock: IC Protection with Tunable Corruptibility and Resilience to SAT and Removal Attacks ”.
  6. [IEEE DFTS 2021] D. Chen, C. Lin, P. A. Beerel, “GF-Flush: A GF(2) Algebraic Attack on Secure Scan Chains”.
  7. [IEEE HOST 2021] Y. Hu.*, Y. Zhang*, K. Yang, D. Chen, P. A. Beerel, P. Nuzzo, “Fun-SAT: Functional Corruptibility-Guided SAT-Based Attack on Sequential Logic Encryption”.

Superconducting Electronics

  1. [ISVLSI 2022] X. Li, M. Pan, T. Liu, P. A. Beerel, “Multi-Phase Clocking for Multi-Threaded Gate-Level-Pipelined Superconductive Logic”, accepted.
  2. [ACM TODAES 2021]  X. Li* , S. N. Shahsavani*, X. Zhou, M. Pedram, and P. A. Beerel, “A Variation-Aware Hold Time Fixing Methodology for Single Flux Quantum Logic Circuits”.
  3. [IEEE Trans. on Circuits and Systems-I 2021] G. Datta, Y. Lin, B. Zhang, P.A. Beerel, “Metastability in Superconducting Single Flux Quantum (SFQ) Logic”
  4. [IEEE Trans. on Applied Superconductivity 2020] G. Datta, A.S. Sudheer, P.H. Srinivas, P.A. Beerel. “Single Flux Quantum (SFQ) First-in-First-Out (FIFO) Synchronizers: New Designs and Paradigms”.
  5. [IEEE International Symposium on Circuits and Systems 2020] G. Datta, P.A. Beerel, “Modeling and Characterization of Metastability in Single Flux Quantum Synchronizers”
  6. [IEEE International Superconductive Electronics Conference 2019] S. Kundu, G.Datta, P.A. Beerel, M. Pedram, “qBSA: Logic Design of a 32-bitBlock-Skewed RSFQ Arithmetic Logic Unit”
  7. [IEEE International Superconductive Electronics Conference 2019] G.Datta, H. Cong, S. Kundu, P. A. Beerel, “qCDC: Metastability-Resilient Synchronization FIFO for SFQ Logic”

Arxiv Preprints:

  1. G. Datta*, S. Lin*, P.A. Beerel, “A High Performance and Robust FIFO Synchronizer-Interface for Crossing Clock Domains in SFQ Logic”, 2021.

Asynchronous VLSI

  1. [IEEE GLSVLSI 2022] M. Herrera and P. A. Beerel. “Radiation Hardening by Design Techniques for the Mutual Exclusion Element”.
  2. [IEEE ASYNC 2020] J. Paykin, B. Huffman, D. M. Zimmerman, P. A. Beerel. “Formal Verification of Flow Equivalence in Desynchronized Designs.” pp. 54-62 ( Best Paper Award Winner ).
  3. [IEEE ASYNC 2020] Felipe Kruentzer, Moises Herrera, Oliver Schrape, P.A. Beerel, Milos Kristic, “Radiation Hardened Click Controllers for Soft Error Resilient Asynchronous Architectures”.
  4. [IEEE PATMOS 2018] Moises Herrera, T. Wang, P.A. Beerel, “Blade-OC Asynchronous Resilient Template”, Platja d’Aro, pp. 147-154.

Interdisciplinary Research

  1. [ICCV Workshop 2023] Y. Hu, X. Ye, Y. Liu, S. Kundu, G. Datta, S. Mutnuri, N. Asavisanu, N. Ayanian, K. Psounis, P. A. Beerel. “FireFly: A Synthetic Dataset for Ember Detection in Wildfire.” 5th Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response (accepted).

For a complete list of publications, visit Professor Beerel’s google scholar page.

University of Southern California