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SOLAR: AI-Powered Speed-of-Light Performance Analysis

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arXiv:2606. 26383v1 Announce Type: new Abstract: How fast could a deep-learning model run on target hardware, and how far is today's implementation from that limit?

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Authors: Qijing Huang, Sana Damani, Zhifan Ye, Athinagoras Skiadopoulos, Siva Kumar Sastry Hari, Jason Clemons, Sahil Modi, Jingquan Wang, Aditya Kane, Edward C Lin, Humphrey Shi, Christos Kozyrakis
Released: 2026-06-26 (13d ago)
Topic: Related to current project
Relation to my work: Read next because SOLAR: AI-Powered Speed-of-Light Performance Analysis overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, soft, eval, source, line, rate, implement, lora. Source: arxiv cs.LG (Machine Learning).
arXiv: 2606.26383

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