9 citations · 20 across the 17 of their papers we have counts for
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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…
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…
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…
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…
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…