Computer Architecture Research

Impact: This research has significant implications for copyright law, AI safety, and the deployment of production LLMs. It demonstrates that current model-level and system-level safeguards are insufficient to prevent extraction of memorized copyrighted training data, even in commercial systems designed with safety measures. The findings are directly relevant to ongoing copyright litigation involving AI companies and may inform future policy decisions about fair use, training data transparency, and the legal treatment of memorized content in AI models. For AI developers, this work highlights the need for more robust safeguards against training data leakage. The relatively low cost of extraction (sometimes under $10) makes this a practical concern for content creators and copyright holders. The research also contributes to the broader understanding of memorization in large language models and provides methodologies for measuring extraction that account for near-verbatim rather than exact matches.

Impact: The Rubin platform addresses the critical challenge of scaling AI factories that continuously convert power, silicon, and data into intelligence for applications like business planning, market analysis, and agentic reasoning. By treating the rack as a coherent machine and co-designing all components, it enables predictable performance and economics in production deployments. The platform's extreme co-design approach delivers practical benefits including reduced GPU count requirements for training, dramatically improved inference throughput for long-context and reasoning workloads, and lower operational costs through improved power efficiency and reliability. The architecture supports secure multi-tenant operations and enables AI factories to scale across geographically dispersed data centers while maintaining deterministic performance.

Impact: These innovations address critical industry challenges in automotive safety, power efficiency, and AI acceleration. The Renesas chiplet architecture enables cost-effective scaling of software-defined vehicles with certified safety, already adopted by Bosch and ZF. Qualcomm's dual-edge clocking can reduce processor power consumption by 40%, directly improving battery life in mobile devices and thermal headroom in data centers. Northwestern's ML-based droop mitigation enables tighter voltage margins, translating to 9-10% performance or efficiency gains. Intel's 3D hybrid bonding demonstrates a path to continued compute density scaling beyond 2D limitations, critical for AI workloads. Together, these advances enable more efficient, safer, and more powerful computing systems across automotive, mobile, and data center domains.

Impact: This work demonstrates that frontier AI models like GPT-5 can substantially accelerate scientific discovery across multiple disciplines. The practical applications include: faster literature searches that overcome vocabulary barriers between fields, rapid hypothesis generation and mechanistic reasoning in biology and immunotherapy, automated verification of mathematical conjectures, and assistance in solving decades-old open problems. The paper shows that GPT-5 can compress months of research reasoning into minutes while maintaining scientific rigor when properly guided by experts. However, it also highlights important limitations including potential attribution errors and the continued necessity of human verification, providing crucial guidance for researchers on how to effectively collaborate with AI while maintaining high scientific standards.

Impact: HipKittens addresses the critical software gap limiting AMD GPU adoption in AI workloads, often called the 'CUDA moat.' By providing accessible C++ programming primitives, it enables developers to write high-performance AMD kernels without resorting to raw assembly. The framework achieves 1.2-10× speedups over existing baselines in various settings and matches AMD's hand-optimized assembly kernels across key operations like GEMM and attention. This work is particularly impactful for democratizing AI hardware access, as AMD MI355X GPUs offer competitive or superior specifications to NVIDIA alternatives (2.5 PFLOPs BF16, 8 TB/s bandwidth, 288 GB memory). The open-source release enables the AI community to leverage diverse hardware platforms, potentially breaking vendor lock-in and accelerating AI development through increased compute availability.

Impact: Nested Learning provides a new theoretical framework for understanding and designing machine learning models with enhanced continual learning capabilities. The HOPE architecture demonstrates practical improvements over Transformers and modern recurrent networks in language modeling tasks, achieving better perplexity and accuracy on benchmark datasets. The framework addresses the static nature of Large Language Models after deployment by enabling online memory consolidation, similar to human brain processes. This has significant implications for developing AI systems that can continually acquire new knowledge beyond their immediate context window, potentially reducing the need for expensive retraining and enabling more adaptive AI systems in production environments.

Impact: PAN enables practical applications in robotics, autonomous systems, and AI planning by providing a general-purpose simulator that can predict future world states based on natural language actions. The model's ability to maintain long-horizon consistency and support interactive simulation makes it valuable for training robotic policies, testing autonomous vehicle scenarios, generating synthetic training data, and enabling agents to perform 'thought experiments' before taking real-world actions. Its open-domain generalization allows deployment across diverse environments without domain-specific retraining, while the natural language action interface makes it accessible for human-in-the-loop applications and planning systems.

Impact: Directly reduces time-to-first-token (TTFT) in production LLM serving systems, enabling better user experience for long-context applications like RAG, summarization, and in-context learning.

Impact: Directly applicable to next-generation GPU architectures, with major vendors expressing interest in the approach for future products.

Impact: Enables cost-effective scaling to 256+ cores without prohibitive directory memory overhead, directly applicable to datacenter processors.

Impact: The Transformer architecture has revolutionized natural language processing and beyond, becoming the foundation for modern large language models like BERT, GPT, and their successors. Its parallel processing capabilities enable efficient training on modern GPU hardware, reducing computational costs and training time significantly. The architecture's success in machine translation demonstrated that attention mechanisms alone could outperform recurrent networks, leading to widespread adoption across various domains including computer vision, speech recognition, and protein folding. The model's interpretability through attention visualizations has also provided insights into how neural networks process sequential data, influencing both research and production systems in industry.