33 citations · 47 across the 16 of their papers we have counts for
7 papers · 1 filter
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 (…
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…
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…
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…
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…