1 citations · 2 across the 3 of their papers we have counts for
4 papers
ModelBlaster: Closing the Performance Gap through Learning-Augmented Agents Across Models, Kernels and Hardware Backends
Shengjun Kris Dong, Dima Nikiforov, Sahil Modi +4
Optimizing accelerator code across hardware architectures is difficult. ML compilers search predefined spaces of schedules and micro-kernels, while LLM-guided optimization can expl…
KernelBlaster: Continual Cross-Task CUDA Optimization via Memory-Augmented In-Context Reinforcement Learning
Kris Shengjun Dong, Sahil Modi, Dima Nikiforov +4
Optimizing CUDA code across multiple generations of GPU architectures is challenging, as achieving peak performance requires an extensive exploration of an increasingly complex, ha…
Characterizing and Optimizing Real-Time Optimal Control for Embedded SoCs
Kris Shengjun Dong, Dima Nikiforov, Widyadewi Soedarmadji +4
Resource-limited robots face significant challenges in executing computationally intensive tasks, such as locomotion and manipulation, particularly for real-time optimal control al…
LLM-Aided Compilation for Tensor Accelerators
Charles Hong, Sahil Bhatia, Altan Haan +4
Hardware accelerators, in particular accelerators for tensor processing, have many potential application domains. However, they currently lack the software infrastructure to suppor…