6 papers
Penrose: Privacy-Preserving Performance Profiling of In-The-Wild GPUs
Ian McDougall, Michael Davies, Rahul Chatterjee +2
GPUs are the dominant platform for many important applications today including deep learning, accelerated computing, and scientific simulation. However, as the complexity of both a…
Pedagogically Motivated and Composable Open-Source RISC-V Processors for Computer Science Education
Ian McDougall, Harish Batchu, Michael Davies +1
While most instruction set architectures (ISAs) are only available to use through the purchase of a restrictive commercial license, the RISC-V ISA presents a free and open-source a…
LIMINAL: Exploring The Frontiers of LLM Decode Performance
Michael Davies, Neal Crago, Karthikeyan Sankaralingam +1
The rapid advancement of Large Language Models (LLMs) necessitates a deep understanding of their fundamental performance limits. This paper investigates the limits of LLM inference…
Kitsune: Enabling Dataflow Execution on GPUs
Michael Davies, Neal Crago, Karthikeyan Sankaralingam +1
State of art DL models are growing in size and complexity, with many modern models also increasing in heterogeneity of behavior. GPUs are still the dominant platform for DL applica…
LookupFFN: Making Transformers Compute-lite for CPU inference
Zhanpeng Zeng, Michael Davies, Pranav Pulijala +2
While GPU clusters are the de facto choice for training large deep neural network (DNN) models today, several reasons including ease of workflow, security and cost have led to effo…
Violet: Architecturally Exposed Orchestration, Movement, and Placement for Generalized Deep Learning
Michael Davies, Karthikeyan Sankaralingam
Deep learning and hardware for it has garnered immense academic and industry interest in the past 5 years, with many novel proposals. However, the state-of-art remains NVIDIA's Ten…