Rathinakumar Appuswamy

Computer Science · Physical Sciences

15h-index6.2kcitations26works0.02yr avg
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Research Topics

Cooperative Communication and Network Coding(10), Advanced Memory and Neural Computing(8), Advanced Neural Network Applications(6), Domain Adaptation and Few-Shot Learning(5), Coding theory and cryptography(4)

Publications26 total

A Scalable NorthPole System with End-to-End Vertical Integration for Low-Latency and Energy-Efficient LLM Inference
arXiv (Cornell University)·2025Open Access
SiLQ: Simple Large Language Model Quantization-Aware Training
arXiv (Cornell University)·2025Open Access
LEARNED STEP SIZE QUANTIZATION
International Conference on Learning Representations·2020· 93 cited
Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Inference
2019· 38 cited
TrueNorth: Accelerating From Zero to 64 Million Neurons in 10 Years
Computer·2019· 231 cited
Learned Step Size Quantization
arXiv (Cornell University)·2019· 296 citedOpen Access
Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference
arXiv (Cornell University)·2018· 46 citedOpen Access
Truenorth ecosystem for brain-inspired computing: scalable systems, software, and applications
IEEE International Conference on High Performance Computing, Data, and Analytics·2016· 43 cited
Convolutional networks for fast, energy-efficient neuromorphic computing
Proceedings of the National Academy of Sciences·2016· 777 citedOpen Access
Structured Convolution Matrices for Energy-efficient Deep learning
arXiv (Cornell University)·2016· 10 citedOpen Access
Deep neural networks are robust to weight binarization and other non-linear distortions
arXiv (Cornell University)·2016· 77 citedOpen Access
Backpropagation for energy-efficient neuromorphic computing
Neural Information Processing Systems·2015· 247 cited
Real-Time Scalable Cortical Computing at 46 Giga-Synaptic OPS/Watt with ~100× Speedup in Time-to-Solution and ~100,000× Reduction in Energy-to-Solution
2014· 73 cited
A million spiking-neuron integrated circuit with a scalable communication network and interface
Science·2014· 4.0k cited
Computing Linear Functions by Linear Coding Over Networks
IEEE Transactions on Information Theory·2013· 44 cited
Cognitive computing systems: Algorithms and applications for networks of neurosynaptic cores
2013· 122 cited
Linear Codes, Target Function Classes, and Network Computing Capacity
IEEE Transactions on Information Theory·2013· 2 citedOpen Access
Linear coding for network computing
2011· 1 cited
Computing linear functions by linear coding over networks
arXiv (Cornell University)·2011· 2 citedOpen Access
Network coding for function computation
2011
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Frequent Co-authors

Dharmendra S. Modha(15), Steven K. Esser(13), John V. Arthur(12), Massimo Franceschetti(10), Paul Merolla(8), Myron Flickner(8), Jeffrey L. McKinstry(8), Andrew S. Cassidy(7), Arnon Amir(7), Brian Taba(6), W. P. Risk(6), Alexander Andreopoulos(6), Pallab Datta(6), Nikhil Karamchandani(6), K. Zeger(6), Jun Sawada(5), Filipp Akopyan(5), Deepika Bablani(5), Tapan K. Nayak(5), Rodrigo Alvarez-Icaza(4)