8 citations · 15 across the 16 of their papers we have counts for
19 papers
DuoDiscover: Scaling Autonomous Discovery Beyond the Evaluation Bottleneck
Shubham Agarwal, Akshat Gupta, Mike Heddes +21
Evaluation has been central to advances in AI, and it is equally critical to autonomous discovery. Yet scaling autonomous discovery to real-world engineering challenges faces a fun…
Coco: An Agentic Copilot for the Hardware--Software Co-Design Lifecycle
Samuel Kushnir, Kavya Sreedhar, Yeshwanth Reddy Pogula +8
Co-designing ML models and the accelerators that run them is an unusual reasoning task: architects must draw confident, high-stakes conclusions about systems that do not yet exist,…
Presage: Prefetch Search via Agent-Guided Experiments
Matthew Giordano, Parthasarathy Ranganathan, Baris Kasikci +1
Data prefetching is an established technique to mitigate cache miss latency and keep the processor saturated with data. Software exists in a unique position to issue prefetches, ha…
ScientistTwo: Pioneering the Human Knowledge Frontier with Autonomous AI
Jaehyun Nam, Jinsung Yoon, Yanzhou Pan +4
Scientific discovery is defined by the ability to identify the boundaries of existing knowledge and venture into unexplored territory. The ultimate vision for AI in science is prob…
Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool
Samuel Kushnir, Kimia Noorbakhsh, Kavya Sreedhar +6
Machine-learning performance modeling is a uniquely hostile terrain for long-lived software: the assumptions baked into today's abstractions are invalidated by tomorrow's models an…
ArchAgent v2: A Case Study with the Data Prefetching Championship
Abraham Gonzalez, Raghav Gupta, Akanksha Jain +12
Agentic artificial intelligence has shown great promise in automating algorithm design, but scaling similar techniques to computer microarchitecture discovery remains challenging d…
Themis: Software-Defined Hardware Prefetching
Keisuke Kamahori, Neil Adit, Kan Zhu +13
Data cache misses represent a significant portion of stall cycles in datacenter workloads. Hardware prefetchers that reduce such stalls by fetching data ahead of time have become i…
Characterizing the Quality Profile of AI-Generated C++ in Production
Michael Tran, Fred Lewis, Kun Yang +5
The widespread integration of AI coding assistants offers undeniable boosts to engineering velocity. Yet, recent studies point to a growing trade-off, revealing persistent challeng…
ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence
Rui Meng, Bhavana Dalvi Mishra, Jiefeng Chen +10
Autonomous research agents produce competitive solutions and professional-looking manuscripts, yet their outputs contain verifiability failures undetectable by surface-level evalua…
Customizing an LLM for Enterprise Software Engineering
Aditya Kini, Satish Chandra, Milad Hashemi +15
Enterprise software development is a continuous evolutionary process, characterized by incremental additions, architectural revisions, production deployments and rigorous maintenan…
A Multi-agent AI System for Deep Learning Model Migration from TensorFlow to JAX
Stoyan Nikolov, Bernhard Konrad, Moritz Gronbach +5
The rapid development of AI-based products and their underlying models has led to constant innovation in deep learning frameworks. Google has been pioneering machine learning usage…
ArchAgent: Agentic AI-driven Computer Architecture Discovery
Raghav Gupta, Akanksha Jain, Abraham Gonzalez +10
Agile hardware design flows are a critically needed force multiplier to meet the exploding demand for compute. Recently, agentic generative AI systems have demonstrated significant…
SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?
Jeffrey Jian Ma, Milad Hashemi, Amir Yazdanbakhsh +5
Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correc…
Instruction Set Migration at Warehouse Scale
Eric Christopher, Kevin Crossan, Wolff Dobson +7
Migrating codebases from one instruction set architecture (ISA) to another is a major engineering challenge. A recent example is the adoption of Arm (in addition to x86) across the…
Measuring the environmental impact of delivering AI at Google Scale
Cooper Elsworth, Keguo Huang, David Patterson +9
The transformative power of AI is undeniable - but as user adoption accelerates, so does the need to understand and mitigate the environmental impact of AI serving. However, no stu…
Silent Data Corruption by 10x Test Escapes Threatens Reliable Computing
Subhasish Mitra, Subho Banerjee, Martin Dixon +5
Too many defective compute chips are escaping existing manufacturing tests -- at least an order of magnitude more than industrial targets across all compute chip types in data cent…
Concorde: Fast and Accurate CPU Performance Modeling with Compositional Analytical-ML Fusion
Arash Nasr-Esfahany, Mohammad Alizadeh, Victor Lee +9
Cycle-level simulators such as gem5 are widely used in microarchitecture design, but they are prohibitively slow for large-scale design space explorations. We present Concorde, a n…
ECO: An LLM-Driven Efficient Code Optimizer for Warehouse Scale Computers
Hannah Lin, Martin Maas, Maximilian Roquemore +17
This paper introduces ECO, a system that automatically modifies source code to improve performance at scale. ECO overcomes the localization problem by combining fleet-wide continuo…
Life-Cycle Emissions of AI Hardware: A Cradle-To-Grave Approach and Generational Trends
Ian Schneider, Hui Xu, Stephan Benecke +4
Specialized hardware accelerators aid the rapid advancement of artificial intelligence (AI), and their efficiency impacts AI's environmental sustainability. This study presents the…