9 citations · 20 across the 17 of their papers we have counts for
17 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…
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…
Catwalk: Unary Top-K for Efficient Ramp-No-Leak Neuron Design for Temporal Neural Networks
Devon Lister, Prabhu Vellaisamy, John Paul Shen +1
Temporal neural networks (TNNs) are neuromorphic neural networks that utilize bit-serial temporal coding. TNNs are composed of columns, which in turn employ neurons as their buildi…
Characterizing and Optimizing LLM Inference Workloads on CPU-GPU Coupled Architectures
Prabhu Vellaisamy, Thomas Labonte, Sourav Chakraborty +3
Large language model (LLM)-based inference workloads increasingly dominate data center costs and resource utilization. Therefore, understanding the inference workload characteristi…
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…
TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering
Prabhu Vellaisamy, Harideep Nair, Vamsikrishna Ratnakaram +2
Temporal Neural Networks (TNNs), a special class of spiking neural networks, draw inspiration from the neocortex in utilizing spike-timings for information processing. Recent works…
tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI
Harideep Nair, Prabhu Vellaisamy, Albert Chen +4
General matrix multiplication (GEMM) is a ubiquitous computing kernel/algorithm for data processing in diverse applications, including artificial intelligence (AI) and deep learnin…
NeRTCAM: CAM-Based CMOS Implementation of Reference Frames for Neuromorphic Processors
Harideep Nair, William Leyman, Agastya Sampath +2
Neuromorphic architectures mimicking biological neural networks have been proposed as a much more efficient alternative to conventional von Neumann architectures for the exploding…
Realtime Person Identification via Gait Analysis
Shanmuga Venkatachalam, Harideep Nair, Prabhu Vellaisamy +3
Each person has a unique gait, i.e., walking style, that can be used as a biometric for personal identification. Recent works have demonstrated effective gait recognition using dee…
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…
Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units
Prabhu Vellaisamy, John Paul Shen
Temporal Neural Networks (TNNs) are spiking neural networks that exhibit brain-like sensory processing with high energy efficiency. This work presents the ongoing research towards…
TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNs
Harideep Nair, Prabhu Vellaisamy, Santha Bhasuthkar +1
Temporal Neural Networks (TNNs), inspired from the mammalian neocortex, exhibit energy-efficient online sensory processing capabilities. Recent works have proposed a microarchitect…
A Microarchitecture Implementation Framework for Online Learning with Temporal Neural Networks
Harideep Nair, John Paul Shen, James E. Smith
Temporal Neural Networks (TNNs) are spiking neural networks that use time as a resource to represent and process information, similar to the mammalian neocortex. In contrast to com…