Priyadarshini Panda
Lloyd F. Hunt Early Career Chair of Electrical & Computer Engineering and Associate Professor of Electrical & Computer Engineering
Designs efficient AI hardware and algorithms, including spiking neural networks and neuromorphic computing.
Priyadarshini Panda's lab at USC works on accelerators, chips & architecture and efficient training & inference. 39 papers since 2023, 15 at top venues, mostly NeurIPS and DAC. 1 PhD student. 11 alumni, most often to Google and Google DeepMind. Applications close .
At a glance
- papers, 2023–2026
- 10/ year
- at T1 venues
- 38%
- PhD students
- 1
- MS students
- 0
- alumni listed
- 11
Papers by yearfrom the lab homepage, 2023–2026
- 1 papers2026 · T1 0 · collecting
- 13 papers2025 · T1 8
- 15 papers2024 · T1 6
- 10 papers2023 · T1 1
T1 venuesOtherVenues: NeurIPS, DAC, ECCV, TMLR, CVPR
Research focuspaper titles that use each topic's words
- spiking16 papers
- Efficient training & inference15 papers
- Accelerators, chips & architecture3 papers
- neuromorphic computing1 paper
- designs efficient0 papers
Paper list
From the lab homepage.
20261 paper
- DATEMD-SNN: Membrane Potential-aware Distillation on Quantized Spiking Neural Network
202513 papers
- SIAM Journal of Scientific ComputingArtificial to Spiking Neural Networks Conversion with Calibration in Scientific Machine Learning
- TMLR (T1)Assortment of attention heads: Accelerating federated PEFT with head pruning and strategic client selection
- NeurIPS (T1)DuoGPT: Training-free Dual Sparsity through Activation-aware Pruning in LLMs
- TMLR (T1)Fast and Cost-effective Speculative Edge-Cloud Decoding with Early Exits
- DAC (T1)FF-INT8: Efficient Forward-Forward DNN Training on Edge Devices with INT8 Precision
- —GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric Calibration
- ISCASLow-power Spike-based Wearable Analytics on RRAM Crossbars
- MLSys (T1)MEADOW: Memory-efficient Dataflow and Data Packing for Low Power Edge LLMs
- NeurIPS (T1)OpenWorldSAM: Extending SAM2 for Universal Image Segmentation with Language Prompts
- —PacQ: A SIMT Microarchitecture for Efficient Dataflow in Hyper-asymmetric GEMMs
- —ReSpike: residual frames-based hybrid spiking neural networks for efficient action recognition
- CVPR (T1)Spiking transformer with spatial-temporal attention
202415 papers
- Nature Machine Intelligence (T1)A collective AI via lifelong learning and sharing
- ICASSP (T1)Energy efficient?—A Hardware Perspective
- Design, Automation, and Test in Europe (DATE) ConferenceFL: Hardware-Aware Low-Precision Federated Learning
- MICRO (T1)for Dual-Sparse Spiking Neural Networks
- DAC (T1)for Energy-efficient ViT Inference
- ECCV (T1)in Low Data Regimes with Generative Synthetic Data
- Asia and South Pacific Design Automation ConferenceINTeger Quantization for Energy Efficient Spiking Neural Networks
- IEEE Transactions on Emerging Topics in Computational IntelligenceLow Power Adversarial Detection for Cloud-Edge Systems
- TCADof Transformers on Memristive Crossbars for Write Noise Mitigation
- ECCV (T1)One-stage Prompt-based Continual Learning
- Neural Networksreally need a large number of visual prompts?
- Frontiers in NeuroscienceRethinking skip connections in Spiking Neural Networks with Time-To-First-Spike
202310 papers
- —DeepCAM: A fully CAM-based inference accelerator with variable hash lengths for energy-efficient deep neural networks
- GLSVLSIExamining the Role and Limits of Batchnorm Optimization to Mitigate Diverse Hardware-noise in In-memory Computing
- Accepted to AAAI Conference on Artificial IntelligenceExploring Temporal Information Dynamics in Spiking Neural Networks
- GLSVLSIHardware Accelerators for Spiking Neural Networks for Energy-Efficient Edge Computing
- Accepted in Design Automation ConferenceInput-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing
- IEEE Transactions on Computer Aided DesignSpikeSim: An end-to-end Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks
- NeurIPS (T1)spiking early exit neural networks
- Transactions in Machine Learning ResearchUncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient
- Accepted in Design Automation ConferenceXPert: Peripheral Circuit & Neural Architecture Co-search for Area and Energy-efficient Xbar-based Computing
- ACM Transactions on Embedded Computing SystemsXploreNAS: Explore Adversarially Robust & Hardware-efficient Neural Architectures for Non-ideal Xbars
Where alumni went11 listed, 63% to industry
Industry 5Academia 3
Most often to
- Google DeepMind
- Meta
Also to
- Fudan University
- Harvard University
- Washington State University
Apply to USCFall 2027
Computer Science PhD (Viterbi)
- Round
- Single round (Dec 15)
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Confirmed on the program page.
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