arXiv subjects

Kris Shengjun Dong

Publications and source records attributed to Kris Shengjun Dong.

3 recordsLinked to original sources

arXiv2026

KernelBlaster: Continual Cross-Task CUDA Optimization via Memory-Augmented In-Context Reinforcement Learning

Optimizing CUDA code across multiple generations of GPU architectures is challenging, as achieving peak performance requires an extensive exploration of an increasingly complex, hardware-specific optimization space. Traditional compilers are constrained by fixed heuristics, whereas finetuning Large Language Models (LLMs) can be expensive. However, agentic workflows for CUDA code optimization have limited ability to aggregate knowledge from prior exploration, leading to biased sampling and suboptimal solutions. We propose KernelBlaster, a Memory-Augmented In-context Reinforcement Learning (MAIC-RL) framework designed to improve CUDA optimization search capabilities of LLM-based GPU coding agents. KernelBlaster enables agents to learn from experience and make systematically informed decisions on future tasks by accumulating knowledge into a retrievable Persistent CUDA Knowledge Base. We propose a novel profile-guided, textual-gradient-based agentic flow for CUDA generation and optimization to achieve high performance across generations of GPU architectures. KernelBlaster guides LLM agents to systematically explore high-potential optimization strategies beyond naive rewrites. Compared to the PyTorch baseline, our method achieves geometric mean speedups of 1.43x, 2.50x, and 1.50x on KernelBench Levels 1, 2, and 3, respectively. We release KernelBlaster as an open-source agentic framework, accompanied by a test harness, verification components, and a reproducible evaluation pipeline.

cs.LG↗

arXiv2025

Characterizing and Optimizing Real-Time Optimal Control for Embedded SoCs

Resource-limited robots face significant challenges in executing computationally intensive tasks, such as locomotion and manipulation, particularly for real-time optimal control algorithms like Model Predictive Control (MPC). This paper provides a comprehensive design space exploration to identify optimal hardware computation architectures for these demanding model-based control algorithms. We profile and optimize representative architectural designs, including general-purpose scalar CPUs, vector processors, and specialized accelerators. By characterizing kernel-level benchmarks and end-to-end robotic scenarios, including a hardware-in-the-loop evaluation on a fabricated RISC-V multi-core vector SoC, we present a quantitative comparison of performance, area, and utilization across distinct architectural design points. Our findings demonstrate that targeted architectural modifications, coupled with deep software and system optimizations, enable up to 3.71x speedups for MPC, resulting in up to 27% system-level power reductions while completing robotic tasks. Finally, we propose a code generation flow designed to simplify the complex engineering effort required for mapping robotic workloads onto specialized architectures.

cs.RO↗

arXiv2025

Certifiable Reachability Learning Using a New Lipschitz Continuous Value Function

We propose a new reachability learning framework for high-dimensional nonlinear systems, focusing on reach-avoid problems. These problems require computing the reach-avoid set, which ensures that all its elements can safely reach a target set despite disturbances within pre-specified bounds. Our framework has two main parts: offline learning of a newly designed reachavoid value function, and post-learning certification. Compared to prior work, our new value function is Lipschitz continuous and its associated Bellman operator is a contraction mapping, both of which improve the learning performance. To ensure deterministic guarantees of our learned reach-avoid set, we introduce two efficient post-learning certification methods. Both methods can be used online for real-time local certification or offline for comprehensive certification. We validate our framework in a 12-dimensional crazyflie drone racing hardware experiment and a simulated 10-dimensional highway take-over example.

eess.SY↗