9 citations · 20 across the 17 of their papers we have counts for
6 papers · 1 filter
Characterization of Request and Token Energy Costs for LLM Inference Workloads on GPU Platforms
Prabhu Vellaisamy, Vanessa Lam, Shawn Blanton +1
Large language model (LLM) inference serving is priced by tokens, but GPU energy is consumed over inference windows. This accounting mismatch makes token-normalized metrics incompl…
TaxBreak: Unmasking the Hidden Costs of LLM Inference Through Overhead Decomposition
Prabhu Vellaisamy, Shreesh Tripathi, Vignesh Natarajan +3
Large Language Model (LLM) inference is widely used in interactive assistants and agentic systems. In latency-sensitive deployments, inference time can become dominated by host-sid…
Mugi: Value Level Parallelism For Efficient LLMs
Daniel Price, Prabhu Vellaisamy, John Shen +1
Value level parallelism (VLP) has been proposed to improve the efficiency of large-batch, low-precision general matrix multiply (GEMM) between symmetric activations and weights. In…
NeuroAI Temporal Neural Networks (NeuTNNs): Microarchitecture and Design Framework for Specialized Neuromorphic Processing Units
Shanmuga Venkatachalam, Prabhu Vellaisamy, Harideep Nair +5
Leading experts from both communities have suggested the need to (re)connect research in neuroscience and artificial intelligence (AI) to accelerate the development of next-generat…
A-Graph: A Unified Graph Representation for Cross-Stack Cost Modeling
Daniel Price, Prabhu Vellaisamy, Patricia Gonzalez +3
As computer systems continue to diversify across technologies, architectures, applications, and beyond, the relevant design space has become larger and more complex. Given such tre…
Exploration of Unary Arithmetic-Based Matrix Multiply Units for Low Precision DL Accelerators
Prabhu Vellaisamy, Harideep Nair, Di Wu +2
General matrix multiplication (GEMM) is a fundamental operation in deep learning (DL). With DL moving increasingly toward low precision, recent works have proposed novel unary GEMM…