13 papers
VTC: DNN Compilation with Virtual Tensors for Data Movement Elimination
Muyan Hu, Ahan Gupta, Jiachen Yuan +7
With the widening gap between compute and memory operation latencies, data movement optimizations have become increasingly important for DNN compilation. Current optimizations such…
Compiler-Driven Approximation Tuning for Hyperdimensional Computing
Xavier Routh, Abdul Rafae Noor, Akash Kothari +4
As Moore's law reaches its physical and economic limits, domain-specific approaches are increasingly employed to accelerate machine learning workloads. Hyperdimensional Computing (…
GRIP: Feedback-Guided Prompt Retrieval for Large Multimodal Models
Garvita Allabadi, Matteo Sodano, Roberto Estevão +4
In-Context Learning (ICL) has become a powerful mechanism for adapting Large Language Models (LLMs) to new tasks without fine-tuning. Extending this concept to Large Multimodal Mod…
Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs
Yifan Zhao, Egan Johnson, Prasanth Chatarasi +2
Operator fusion has become a key optimization for deep learning, which combines multiple deep learning operators to improve data reuse and reduce global memory transfers. However,…
MIRAGE: A Benchmark for Multimodal Information-Seeking and Reasoning in Agricultural Expert-Guided Conversations
Vardhan Dongre, Chi Gui, Shubham Garg +4
We introduce MIRAGE, a new benchmark for multimodal expert-level reasoning and decision-making in consultative interaction settings. Designed for the agriculture domain, MIRAGE cap…
SLMFix: Leveraging Small Language Models for Error Fixing with Reinforcement Learning
David Jiahao Fu, Aryan Gupta, Aaron Councilman +3
Recent advancements in large language models (LLMs) have shown very impressive capabilities in code generation across many programming languages. However, even state-of-the-art LLM…