9 citations · 20 across the 17 of their papers we have counts for
6 papers · 1 filter
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…