2 papers
cs.AR2026
Characterizing the Impact of NVFP4 Quantization for Low-Power Edge AI Deployment
Ovishake Sen, Venkata Nithin Kamineni, Daniel Lobo +3
Energy-efficient neural-network inference at the edge requires reducing arithmetic cost, memory traffic, computation energy, and storage overhead while maintaining acceptable accur…
cs.CR2026
HAMLOCK: HArdware-Model LOgically Combined attacK
Sanskar Amgain, Daniel Lobo, Atri Chatterjee +2
The growing use of third-party hardware accelerators (e.g., FPGAs, ASICs) for deep neural networks (DNNs) introduces new security vulnerabilities. Conventional model-level backdoor…