Author

Kevin Skadron

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Hardware Characterization of Di ! usion vs. Autoregressive Language Model Inference: Compute vs. Memory Bottlenecks

A hardware-level characterization of representative di ! usion language models is presented and it is shown that, although MDLMs and AR models share similar Transformer building blocks, di ! usion inference exhibits fundamentally different bottlenecks at the hardware level, breaking the assumptions underlying modern AR...

Zhen-Xing Fan, Kevin Skadron · 0 citations

Reimagining LLM Inference Infrastructure with Memory-Centric KV Cache Servers

It is argued that a KV cache server — disaggregated, CXL-attached memory device(s) with optional near-memory compute—is the correct infrastructure primitive for the next generation of AI data centers.

Khyati Kiyawat, Kevin Skadron, UALinkNVlink · 0 citations

Tailoring LLM Weight Compression For PIM Architectures

This work investigates lightweight BF16 weight compression schemes tailored for PIM-based LLM inference by focusing on exponent-oriented compression methods that exploit the locality and redundancy present in BF16 exponent fields.

Sabiha Tajdari, Akhil Shekar, Kevin Skadron et al. · 0 citations