Projects
Six research projects from the Penn State years plus the current ongoing work at Arm. Each card links to a deeper write-up.
- 01 / 07
ICLR 2025 NExUME
Intermittency-aware DNN training for energy-harvesting micro-computers.
Dynamically adjusts dropout and quantization in response to intermittent energy. DynFit optimizer + DynInfer scheduler. Up to 22% accuracy gain at <5% compute overhead.
Energy HarvestingIntermittent ComputingDNN TrainingQuantizationread more →
- 02 / 07
PACT 2025 Salient Store
Computational storage for continuous-learning edge servers.
FPGA-accelerated computational storage devices handle compression, encryption, and redundancy near the data. 6× throughput, 6.1× less data movement.
Computational StorageContinuous LearningEdgeFPGAread more →
- 03 / 07
HPCA 2024 Usás
Battery-free continuous learning on solar-powered edge servers.
A morphable accelerator that adapts to fluctuating solar power, paired with teacher-student training. Hundreds of kWh/year saved per device, no battery required.
Sustainable ComputingContinuous LearningSolarEdgeread more →
- 04 / 07
arXiv 2022 Seeker
Coreset-based partial inference on energy-harvesting wearables.
Sensors compute compressed coresets locally and ship only those to the host for final inference. Up to 8.9× communication reduction with near-baseline accuracy.
EH-WSNHARCoresetEdgeread more →
- 05 / 07
DATE 2021 ★ Best Paper Nominee Origin
Scheduling and ensemble learning for HAR on energy-harvesting body networks.
Coordinates intermittent sensors across the body for human activity recognition. 2.5–5% accuracy gain under tight harvested-power constraints.
EH-WSNHAREnsemble LearningSchedulingread more →
- 06 / 07
under review Prophet
Neural expert prediction for efficient mixture-of-experts inference.
Predicts which experts will be activated before the routing layer fires, letting MoE inference avoid round-trips to dormant experts.
MoELLM InferenceML SystemsEfficient Inferenceread more →
- 07 / 07
ongoing SoC Performance & Power Modeling at Arm
Pre-silicon perf/power analysis for next-generation heterogeneous SoCs.
Current work on the SoC Architecture Performance team — informing trade-off decisions for compute subsystems targeting Edge AI, IoT, and automotive workloads. Public-facing description; no internal details.
SoCPerformance ModelingPower ModelingEdge AIread more →