109 citations · 616 across the 40 of their papers we have counts for
8 papers · 1 filter
Cyclotron: Compilation of Recurrences to Distributed and Systolic Architectures
Shiv Sundram, Akhilesh Balasingam, Nathan Zhang +2
We present Cyclotron, a framework and compiler for using recurrence equations to express streaming dataflow algorithms, which then get portably compiled to distributed topologies o…
AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization
Genghan Zhang, Shaowei Zhu, Anjiang Wei +6
We present AccelOpt, a self-improving large language model (LLM) agentic system that autonomously optimizes kernels for emerging AI acclerators, eliminating the need for expert-pro…
Streaming Tensor Programs: A Streaming Abstraction for Dynamic Parallelism
Gina Sohn, Genghan Zhang, Konstantin Hossfeld +5
Dynamic behaviors are becoming prevalent in tensor applications, like machine learning, where many widely used models contain data-dependent tensor shapes and control flow. However…
FuseFlow: A Fusion-Centric Compilation Framework for Sparse Deep Learning on Streaming Dataflow
Rubens Lacouture, Nathan Zhang, Ritvik Sharma +4
As deep learning models scale, sparse computation and specialized dataflow hardware have emerged as powerful solutions to address efficiency. We propose FuseFlow, a compiler that c…
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
Qizheng Zhang, Changran Hu, Shubhangi Upasani +10
Large language model (LLM) applications such as agents and domain-specific reasoning increasingly rely on context adaptation: modifying inputs with instructions, strategies, or evi…
Agentic Plan Caching: Test-Time Memory for Fast and Cost-Efficient LLM Agents
Qizheng Zhang, Michael Wornow, Gerry Wan +1
LLM-based agent applications have shown increasingly remarkable capabilities in complex workflows but incur substantial costs and latency due to extensive planning and reasoning re…
LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits
Zikai Zhou, Qizheng Zhang, Hermann Kumbong +1
Fine-tuning large language models (LLMs) is increasingly costly as models scale to hundreds of billions of parameters, and even parameter-efficient fine-tuning (PEFT) methods like…
Adaptive Self-improvement LLM Agentic System for ML Library Development
Genghan Zhang, Weixin Liang, Olivia Hsu +1
ML libraries, often written in architecture-specific programming languages (ASPLs) that target domain-specific architectures, are key to efficient ML systems. However, writing thes…