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Payman Behnam

2 accepted papers

2025

RocketKV: Accelerating Long-Context LLM Inference via Two-Stage KV Cache Compression

ICML 2025poster

Transformer-based Large Language Models rely critically on the KV cache to efficiently handle extended contexts during the decode phase. Yet, the size of the KV cache grows proportionally with the input length, burdening both memory bandwidth and capacity as decoding progresses. To address this chal…

Cited by 0SourcePDFScholar
2024

SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-Device Inference

ECCV 2024poster

"Neural Architecture Search (NAS) for Federated Learning (FL) is an emerging field. It automates the design and training of Deep Neural Networks (DNNs) when data cannot be centralized due to privacy, communication costs, or regulatory restrictions. Recent federated NAS methods not only reduce manual…