Implementation Issues In ERP. - Rathinakumar Appuswamy (ICSTM, 2000)

Optimality of Linear Codes for Broadcast-Mode Multicast Networks. - Rathinakumar Appuswamy; Massimo Franceschetti; Kenneth Zeger (ISIT, 2006)

A New Framework for Constructing Mutually Orthogonal Complementary Sets and ZCZ Sequences. - Rathinakumar Appuswamy; Ajit Kumar Chaturvedi (IEEE Trans. Inf. Theory, 2006)

QoS guarantee and provisioning for realtime digital video over mobile ad hoc cdma networks with cross-layer design. - Qi Qu; Rathinakumar Appuswamy; Yee Sin Chan (IEEE Wirel. Commun., 2006)

Network coding for computing. - Rathinakumar Appuswamy; Massimo Franceschetti; Nikhil Karamchandani; Kenneth Zeger (Allerton, 2008)

Complete Mutually Orthogonal Golay Complementary Sets From Reed-Muller Codes. - Rathinakumar Appuswamy; Ajit Kumar Chaturvedi (IEEE Trans. Inf. Theory, 2008)

Network computing capacity for the reverse butterfly network. - Rathinakumar Appuswamy; Massimo Franceschetti; Nikhil Karamchandani; Kenneth Zeger (ISIT, 2009)

Distributed computation of symmetric functions with binary inputs. - Nikhil Karamchandani; Rathinakumar Appuswamy; Massimo Franceschetti (ITW, 2009)

Network Coding for Computing Part I : Cut-Set Bounds - Rathinakumar Appuswamy; Massimo Franceschetti; Nikhil Karamchandani; Kenneth Zeger (CoRR, 2009)

Computing linear functions with linear network codes. - Rathinakumar Appuswamy; Massimo Franceschetti (Allerton, 2010)

Linear coding for network computing. - Rathinakumar Appuswamy; Massimo Franceschetti; Nikhil Karamchandani; Kenneth Zeger (ISIT, 2011)

Network coding for computing: Linear codes - Rathinakumar Appuswamy; Massimo Franceschetti; Nikhil Karamchandani; Kenneth Zeger (CoRR, 2011)

Network Coding for Computing: Cut-Set Bounds. - Rathinakumar Appuswamy; Massimo Franceschetti; Nikhil Karamchandani; Kenneth Zeger (IEEE Trans. Inf. Theory, 2011)

Time and Energy Complexity of Function Computation Over Networks. - Nikhil Karamchandani; Rathinakumar Appuswamy; Massimo Franceschetti (IEEE Trans. Inf. Theory, 2011)

Network coding for function computation. - Rathinakumar Appuswamy (2011)

Cognitive computing systems: Algorithms and applications for networks of neurosynaptic cores. - Steven K. Esser; Alexander Andreopoulos; Rathinakumar Appuswamy; Pallab Datta; Davis Barch; Arnon Amir; John V. Arthur; Andrew Cassidy; Myron Flickner; Paul Merolla; Shyamal Chandra; Nicola Basilico; et al. (IJCNN, 2013)

Linear Codes, Target Function Classes, and Network Computing Capacity. - Rathinakumar Appuswamy; Massimo Franceschetti; Nikhil Karamchandani; Kenneth Zeger (IEEE Trans. Inf. Theory, 2013)

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. - Andrew S. Cassidy; Rodrigo Alvarez-Icaza; Filipp Akopyan; Jun Sawada; John V. Arthur; Paul Merolla; Pallab Datta; Marc González Tallada; Brian Taba; Alexander Andreopoulos; Arnon Amir; Steven K. Esser; et al. (SC, 2014)

Computing Linear Functions by Linear Coding Over Networks. - Rathinakumar Appuswamy; Massimo Franceschetti (IEEE Trans. Inf. Theory, 2014)

Backpropagation for Energy-Efficient Neuromorphic Computing. - Steven K. Esser; Rathinakumar Appuswamy; Paul Merolla; John V. Arthur; Dharmendra S. Modha (NIPS, 2015)

Visual saliency on networks of neurosynaptic cores. - Alexander Andreopoulos; Brian Taba; Andrew S. Cassidy; Rodrigo Alvarez-Icaza; Myron Flickner; William P. Risk; Arnon Amir; Paul Merolla; John V. Arthur; David J. Berg; Jeffrey A. Kusnitz; Pallab Datta; et al. (IBM J. Res. Dev., 2015)

Truenorth ecosystem for brain-inspired computing: scalable systems, software, and applications. - Jun Sawada; Filipp Akopyan; Andrew S. Cassidy; Brian Taba; Michael V. DeBole; Pallab Datta; Rodrigo Alvarez-Icaza; Arnon Amir; John V. Arthur; Alexander Andreopoulos; Rathinakumar Appuswamy; Heinz Baier; et al. (SC, 2016)

Structured Convolution Matrices for Energy-efficient Deep learning. - Rathinakumar Appuswamy; Tapan K. Nayak; John V. Arthur; Steven K. Esser; Paul Merolla; Jeffrey L. McKinstry; Timothy Melano; Myron Flickner; Dharmendra S. Modha (CoRR, 2016)

Deep neural networks are robust to weight binarization and other non-linear distortions. - Paul Merolla; Rathinakumar Appuswamy; John V. Arthur; Steven K. Esser; Dharmendra S. Modha (CoRR, 2016)

Convolutional networks for fast, energy-efficient neuromorphic computing. - Steven K. Esser; Paul A. Merolla; John V. Arthur; Andrew S. Cassidy; Rathinakumar Appuswamy; Alexander Andreopoulos; David J. Berg; Jeffrey L. McKinstry; Timothy Melano; Davis R. Barch; Carmelo di Nolfo; Pallab Datta; et al. (Proc. Natl. Acad. Sci. USA, 2016)

Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference. - Jeffrey L. McKinstry; Steven K. Esser; Rathinakumar Appuswamy; Deepika Bablani; John V. Arthur; Izzet B. Yildiz; Dharmendra S. Modha (CoRR, 2018)

Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Inference. - Jeffrey L. McKinstry; Steven K. Esser; Rathinakumar Appuswamy; Deepika Bablani; John V. Arthur; Izzet B. Yildiz; Dharmendra S. Modha (EMC2@NeurIPS, 2019)

TrueNorth: Accelerating From Zero to 64 Million Neurons in 10 Years. - Michael V. DeBole; Brian Taba; Arnon Amir; Filipp Akopyan; Alexander Andreopoulos; William P. Risk; Jeff Kusnitz; Carlos Ortega Otero; Tapan K. Nayak; Rathinakumar Appuswamy; Peter J. Carlson; Andrew S. Cassidy; et al. (Computer, 2019)

Learned Step Size quantization. - Steven K. Esser; Jeffrey L. McKinstry; Deepika Bablani; Rathinakumar Appuswamy; Dharmendra S. Modha (ICLR, 2020)

IBM NorthPole Neural Inference Machine. - Dharmendra S. Modha; Filipp Akopyan; Alexander Andreopoulos; Rathinakumar Appuswamy; John V. Arthur; Andrew S. Cassidy; Pallab Datta; Michael V. DeBole; Steven K. Esser; Carlos Ortega Otero; Jun Sawada; Brian Taba; et al. (HCS, 2023)

Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference. - Deepika Bablani; Jeffrey L. McKinstry; Steven K. Esser; Rathinakumar Appuswamy; Dharmendra S. Modha (CoRR, 2023)

Breakthrough Edge AI Inference Performance Using Northpole in 3U Vpx Form Factor. - Filipp Akopyan; William P. Risk; John V. Arthur; Andrew S. Cassidy; Michael V. DeBole; Carlos Ortega Otero; Jun Sawada; Evan Colgan; Michael E. Criscolo; Phillip V. Mann; Heinz Baier; Kai Schleupen; et al. (HPEC, 2024)

Breakthrough Low-Latency, High-Energy-Efficiency LLM Inference Performance Using NorthPole. - Rathinakumar Appuswamy; Michael V. DeBole; Brian Taba; Steven K. Esser; Andrew S. Cassidy; Arnon Amir; Alexander Andreopoulos; Deepika Bablani; Pallab Datta; Jeffrey A. Kusnitz; Nathaniel J. McClatchey; Neil McGlohon; et al. (HPEC, 2024)

11.4 IBM NorthPole: An Architecture for Neural Network Inference with a 12nm Chip. - Andrew S. Cassidy; John V. Arthur; Filipp Akopyan; Alexander Andreopoulos; Rathinakumar Appuswamy; Pallab Datta; Michael V. DeBole; Steven K. Esser; Carlos Ortega Otero; Jun Sawada; Brian Taba; Arnon Amir; et al. (ISSCC, 2024)

The NorthPole Validator: A Cycle-Accurate Simulator for HW/SW Codesign of a Prescheduled Neural Inference Accelerator. - Alexander Andreopoulos; Michael V. DeBole; Jeffrey A. Kusnitz; Nathaniel J. McClatchey; Tapan K. Nayak; Daniel F. Smith; Brian Taba; Filipp Akopyan; Rathinakumar Appuswamy; John V. Arthur; Andrew S. Cassidy; Pallab Datta; et al. (HPEC, 2025)

SiLQ: Simple Large Language Model Quantization-Aware Training. - Steven K. Esser; Jeffrey L. McKinstry; Deepika Bablani; Rathinakumar Appuswamy; Dharmendra S. Modha (CoRR, 2025)