3 papers
cs.LG2026
Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels
Plawan Kumar Rath, Rahul Maliakkal
Large Language Models are routinely compressed via post-training quantization to reduce inference costs and memory footprint for cloud and edge deployment, yet the impact of this c…
cs.LG2026
Weight Pruning Amplifies Bias: A Multi-Method Study of Compressed LLMs for Edge AI
Plawan Kumar Rath, Rahul Maliakkal
Weight pruning is widely advocated for deploying Large Language Models on resource-constrained IoT and edge devices, yet its impact on model fairness remains poorly understood. We…
cs.AR2025
Sustainable AI Training via Hardware-Software Co-Design on NVIDIA, AMD, and Emerging GPU Architectures
Yashasvi Makin, Rahul Maliakkal
In particular, large-scale deep learning and artificial intelligence model training uses a lot of computational power and energy, so it poses serious sustainability issues. The fas…