4 citations · 8 across the 6 of their papers we have counts for
6 papers
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…
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…
Tempus Core: Area-Power Efficient Temporal-Unary Convolution Core for Low-Precision Edge DLAs
Prabhu Vellaisamy, Harideep Nair, Thomas Kang +6
The increasing complexity of deep neural networks (DNNs) poses significant challenges for edge inference deployment due to resource and power constraints of edge devices. Recent wo…
tubGEMM: Energy-Efficient and Sparsity-Effective Temporal-Unary-Binary Based Matrix Multiply Unit
Prabhu Vellaisamy, Harideep Nair, Joseph Finn +7
General Matrix Multiplication (GEMM) is a ubiquitous compute kernel in deep learning (DL). To support energy-efficient edge-native processing, new GEMM hardware units have been pro…
Commercial Evaluation of Zero-Skipping MAC Design for Bit Sparsity Exploitation in DL Inference
Harideep Nair, Prabhu Vellaisamy, Tsung-Han Lin +3
General Matrix Multiply (GEMM) units, consisting of multiply-accumulate (MAC) arrays, perform bulk of the computation in deep learning (DL). Recent work has proposed a novel MAC de…