From the 1 of 4 linked papers with an AI index.
4 papers
The Joint Effect of Quantization and Sampling Temperature on LLM Safety Alignment: A Factorial Analysis
Hari Prasad, Ritam Pal
The paper investigates how model quantization and higher sampling temperatures jointly affect the safety alignment of instruction-tuned large language models, finding that quantiza…
LLM Inference at the Edge: Mobile, NPU, and GPU Performance Efficiency Trade-offs Under Sustained Load
Pranay Tummalapalli, Sahil Arayakandy, Ritam Pal +1
Deploying large language models on-device for always-on personal agents demands sustained inference from hardware tightly constrained in power, thermal envelope, and memory. We ben…
Identifying fatigue crack initiation through analytical calculation of temporal compliance calibrated with Computed Tomography
Ritam Pal, Amrita Basak
Fatigue failure is ubiquitous in engineering applications. While the total fatigue life is critical to understanding a component's operational life, for safety, regulatory complian…
Surface roughness-informed fatigue life prediction of L-PBF Hastelloy X at elevated temperature
Ritam Pal, Brandon Kemerling, Daniel Ryan +2
Additive manufacturing, especially laser powder bed fusion (L-PBF), is widely used for fabricating metal parts with intricate geometries. However, parts produced via L-PBF suffer f…