5 papers
ArchEval: Measuring AI Agents as Computer Architects
Chenyu Wang, Zishen Wan, Jeffrey Ma +8
Computer architecture has long used benchmarks to make progress measurable. LLM agents create a different measurement problem: success is not merely writing code or tuning paramete…
Lifetime-Aware Design for Item-Level Intelligence at the Extreme Edge
Shvetank Prakash, Andrew Cheng, Olof Kindgren +13
We present FlexiFlow, a lifetime-aware design framework for item-level intelligence (ILI) where computation is integrated directly into disposable products like food packaging and…
Datasheets for Machine Learning Sensors
Matthew Stewart, Yuke Zhang, Pete Warden +12
Machine learning (ML) is becoming prevalent in embedded AI sensing systems. These "ML sensors" enable context-sensitive, real-time data collection and decision-making across divers…
QuArch: A Benchmark for Evaluating LLM Reasoning in Computer Architecture
Shvetank Prakash, Andrew Cheng, Arya Tschand +25
The field of computer architecture, which bridges high-level software abstractions and low-level hardware implementations, remains absent from current large language model (LLM) ev…
QuArch: A Question-Answering Dataset for AI Agents in Computer Architecture
Shvetank Prakash, Andrew Cheng, Jason Yik +14
We introduce QuArch, a dataset of 1500 human-validated question-answer pairs designed to evaluate and enhance language models' understanding of computer architecture. The dataset c…