Power efficient event detection scheme in wireless sensor networks for railway bridge health monitoring system. - Soumendu Kumar Ghosh; Maroju Suman; Raja Datta; Prabir Kumar Biswas (IEEE ANTS, 2014)

CEEDS: A Cost Effective Event Detection System for Energy Efficient Railway Bridge Monitoring with Wireless Sensor Network. - Soumendu Kumar Ghosh; Maroju Suman; Raja Datta; Prabir Kumar Biswas (EAI Endorsed Trans. Ubiquitous Environ., 2016)

Railway bridge health monitoring system using smart wireless sensor network: demo. - Sathvik Dev Velagandula; Nirjhar Dhang; Raja Datta; Soumendu Kumar Ghosh; Maroju Suman (WISEC, 2017)

Approximate inference systems (AxIS): end-to-end approximations for energy-efficient inference at the edge. - Soumendu Kumar Ghosh; Arnab Raha; Vijay Raghunathan (ISLPED, 2020)

Special Session: Approximate TinyML Systems: Full System Approximations for Extreme Energy-Efficiency in Intelligent Edge Devices. - Arnab Raha; Soumendu Kumar Ghosh; Debabrata Mohapatra; Deepak A. Mathaikutty; Raymond Sung; Cormac Brick; Vijay Raghunathan (ICCD, 2021)

Energy-Efficient Approximate Edge Inference Systems. - Soumendu Kumar Ghosh; Arnab Raha; Vijay Raghunathan (ACM Trans. Embed. Comput. Syst., 2023)

SwiSS: Switchable Single-Sided Sparsity-based DNN Accelerators. - Shamik Kundu; Soumendu Kumar Ghosh; Arnab Raha; Deepak A. Mathaikutty (ISLPED, 2024)

HARVEST: Towards Efficient Sparse DNN Accelerators using Programmable Thresholds. - Soumendu Kumar Ghosh; Shamik Kundu; Arnab Raha; Deepak A. Mathaikutty; Vijay Raghunathan (VLSID, 2024)

FlexNN: A Dataflow-aware Flexible Deep Learning Accelerator for Energy-Efficient Edge Devices. - Arnab Raha; Deepak A. Mathaikutty; Soumendu Kumar Ghosh; Shamik Kundu (CoRR, 2024)

PArtNNer: Platform-Agnostic Adaptive Edge-Cloud DNN Partitioning for Minimizing End-to-End Latency. - Soumendu Kumar Ghosh; Arnab Raha; Vijay Raghunathan; Anand Raghunathan (ACM Trans. Embed. Comput. Syst., 2024)

Toward Energy-Efficient Collaborative Inference Using Multisystem Approximations. - Arghadip Das; Soumendu Kumar Ghosh; Arnab Raha; Vijay Raghunathan (IEEE Internet Things J., 2024)

LLM-NPU: Towards Efficient Foundation Model Inference on Low-Power Neural Processing Units. - Arnab Raha; Souvik Kundu 009; Sharath Nittur Sridhar; Shamik Kundu; Soumendu Kumar Ghosh; Alessandro Palla; Arghadip Das; Darren Crews; Deepak A. Mathaikutty (COINS, 2025)

GraNNite: Enabling High-Performance Execution of Graph Neural Networks on Resource-Constrained Neural Processing Units. - Arghadip Das; Shamik Kundu; Arnab Raha; Soumendu Kumar Ghosh; Deepak Mathaikutty; Vijay Raghunathan (CoRR, 2025)

XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units. - Arghadip Das; Arnab Raha; Shamik Kundu; Soumendu Kumar Ghosh; Deepak Mathaikutty; Vijay Raghunathan (CoRR, 2025)

Towards Energy-Accuracy Scalable Multimodal Cognitive Systems. - Soumendu Kumar Ghosh; Arghadip Das; Arnab Raha; Vijay Raghunathan (IEEE Embed. Syst. Lett., 2025)