33 citations · 47 across the 16 of their papers we have counts for
19 papers
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…
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…
SLMFix: Leveraging Small Language Models for Domain Specific Language Error Fixing with Reinforcement Learning
David Jiahao Fu, Aryan Gupta, Aaron Councilman +3
Large language models (LLMs) have shown impressive capabilities in code generation across many programming languages but even state-of-the-art LLMs generate programs that contain s…
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,…
Towards Formal Verification of LLM-Generated Code from Natural Language Prompts
Aaron Councilman, David Jiahao Fu, Aryan Gupta +4
In the past few years LLMs have emerged as a tool that can aid programmers by taking natural language descriptions and generating code based on it. However, the reliability of LLM…
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…
AgMMU: A Comprehensive Agricultural Multimodal Understanding Benchmark
Aruna Gauba, Irene Pi, Yunze Man +3
We present AgMMU, a challenging real-world benchmark for evaluating and advancing vision-language models (VLMs) in the knowledge-intensive domain of agriculture. Unlike prior datas…
Hercules: A Compiler for Productive Programming of Heterogeneous Systems
Russel Arbore, Aaron Councilman, Xavier Routh +3
Modern computing systems increasingly rely on composing heterogeneous devices to improve performance and efficiency. Programming these systems is often unproductive: algorithm impl…
LEWIS (LayEr WIse Sparsity) -- A Training Free Guided Model Merging Approach
Hetarth Chopra, Vidhi Rambhia, Vikram Adve
As specialized large language models (LLMs) become increasingly prevalent, model merging methods are being used to combine them to create a single multi-task model without requirin…
Transforming the Hybrid Cloud for Emerging AI Workloads
Deming Chen, Alaa Youssef, Ruchi Pendse +42
This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet th…
HPVM-HDC: A Heterogeneous Programming System for Accelerating Hyperdimensional Computing
Russel Arbore, Xavier Routh, Abdul Rafae Noor +7
Hyperdimensional Computing (HDC), a technique inspired by cognitive models of computation, has been proposed as an efficient and robust alternative basis for machine learning. HDC…
Generalized Open-World Semi-Supervised Object Detection
Garvita Allabadi, Ana Lucic, Siddarth Aananth +3
Traditional semi-supervised object detection methods assume a fixed set of object classes (in-distribution or ID classes) during training and deployment, which limits performance i…
Coordinated Science Laboratory 70th Anniversary Symposium: The Future of Computing
Klara Nahrstedt, Naresh Shanbhag, Vikram Adve +25
In 2021, the Coordinated Science Laboratory CSL, an Interdisciplinary Research Unit at the University of Illinois Urbana-Champaign, hosted the Future of Computing Symposium to cele…
Trireme: Exploring Hierarchical Multi-Level Parallelism for Domain Specific Hardware Acceleration
Georgios Zacharopoulos, Adel Ejjeh, Ying Jing +9
The design of heterogeneous systems that include domain specific accelerators is a challenging and time-consuming process. While taking into account area constraints, designers mus…
Studying the Potential of Automatic Optimizations in the Intel FPGA SDK for OpenCL
Adel Ejjeh, Vikram Adve, Rob Rutenbar
High Level Synthesis (HLS) tools, like the Intel FPGA SDK for OpenCL, improve design productivity and enable efficient design space exploration guided by simple program directives…
Statistical Program Slicing: a Hybrid Slicing Technique for Analyzing Deployed Software
Bogdan Alexandru Stoica, Swarup K. Sahoo, James R. Larus +1
Dynamic program slicing can significantly reduce the code developers need to inspect by narrowing it down to only a subset of relevant program statements. However, despite an exten…
DLVM: A modern compiler infrastructure for deep learning systems
Richard Wei, Lane Schwartz, Vikram Adve
Deep learning software demands reliability and performance. However, many of the existing deep learning frameworks are software libraries that act as an unsafe DSL in Python and a…
HPVM: A Portable Virtual Instruction Set for Heterogeneous Parallel Systems
Prakalp Srivastava, Maria Kotsifakou, Vikram Adve
We describe a programming abstraction for heterogeneous parallel hardware, designed to capture a wide range of popular parallel hardware, including GPUs, vector instruction sets an…