Weichu Yang | Searcharxiv

arXiv2026

Can LLMs Perform Deep Technical Comprehension of Computer Architecture Papers?

Can large language models perform deep technical comprehension of computer architecture papers -- not summarization, but structured critique that names the core mechanism, surfaces buried assumptions, and connects a contribution beyond its own scope? We study Gauntlet, an open-source pipeline that analyzes a paper through five independent expert-persona reviewers and an adversarial synthesis stage. On 20 ISCA 2025 and HPCA 2026 papers, ten researchers each wrote their own analyses and then judged, for papers other than their own, the human analysis against Gauntlet's. Across the 20 comparisons evaluators preferred Gauntlet in 15 (human in 4, one tie); its advantage is significant on per-analyst totals (paired Wilcoxon, p < 0.01) and largest on Critical Rigor, vanishing only on Calibration. Where humans win, it is on trust and usefulness rather than depth: a confident wrong claim, a mechanism described but not taught, or unprioritized breadth. A 98-paper automated ablation shows the gain comes from the multi-agent structure -- the pipeline beats the same model run as a single rich-persona agent on 96% of papers -- and specifically from its synthesis pass. We release all analyses, scores, and the rubric as a community resource.

cs.CY↗

arXiv2025

NeuronMLP: Efficient LLM Inference via Singular Value Decomposition Compression and Tiling on AWS Trainium

Emerging AI accelerators have started to gain attention and offer new opportunities for efficient inference of large language models (LLMs). Trainium, an AI accelerator recently developed by Amazon Web Services (AWS), provides an attractive option for LLM inference through its heterogeneous architecture. However, leveraging Trainium architecture for high performance can be challenging because of its systolic array architecture and special requirement on data layout. In this paper, we propose NeuronMLP, an efficient LLM inference method based on Singular Value Decomposition (SVD) compression and tiling on AWS Trainium. We introduce a series of techniques customized to Trainium based on kernel fusion and novel caching strategies to reduce data movement across the software-managed memory hierarchy, maximize SRAM bandwidth, and avoid expensive matrix transpose. The proposed method is specifically optimized for multi-layer perceptron (MLP) layers in LLMs, which serve as a critical computational kernel for inference on Trainium. Evaluating on nine datasets and six recent LLMs, we show that NeuronMLP significantly outperforms the state-of-the-art Neuron Kernel Interface (NKI)-based matrix multiplication (matmul) kernel implemented by AWS on Trainium: at the kernel level, it achieves an average 1.35x speedup, which translates to an average 1.21x speedup for end-to-end LLM inference, under a compression ratio of 0.05.

cs.CL↗