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cs.CR2026
TEE-X: TEE-aware Acceleration Framework for Large Vision Models at the Edge
Kurt M Wilson, Mohaiminul Al Nahian, Abeer Matar A. Almalky +5
Despite their remarkable success, machine learning models, particularly in vision applications, are alarmingly vulnerable to a range of security threats. One key factor in the atta…
cs.AR2026
SPARQLe: Sub-Precision Activation Representation for Quantized LLM Inference
Aradhana Mohan Parvathy, Soumendu Kumar Ghosh, Shamik Kundu +4
The rapid growth in sizes of Large language models (LLMs) results in high compute and memory costs during inference. Quantization has been a significant pathway to addressing this…