Steven K. Esser

Engineering · Physical Sciences

19h-index7.4kcitations34works3.82yr avg
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Research Topics

Advanced Memory and Neural Computing(16), Advanced Neural Network Applications(7), Neural dynamics and brain function(6), Domain Adaptation and Few-Shot Learning(6), Neuroscience and Neural Engineering(5)

Publications34 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
Breakthrough Edge AI Inference Performance Using Northpole in 3U Vpx Form Factor
2024· 2 cited
Breakthrough Low-Latency, High-Energy-Efficiency LLM Inference Performance Using NorthPole
2024· 3 cited
11.4 IBM NorthPole: An Architecture for Neural Network Inference with a 12nm Chip
2024· 14 cited
Neural inference at the frontier of energy, space, and time
Science·2023· 85 cited
IBM NorthPole Neural Inference Machine
2023· 10 cited
Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference
arXiv (Cornell University)·2023· 2 citedOpen Access
A system architecture ensuring consistency among distributed, heterogeneous information models for civil infrastructure projects
2021· 4 citedOpen 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
Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing
arXiv (Cornell University)·2018· 3 citedOpen Access
Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference
arXiv (Cornell University)·2018· 46 citedOpen Access
Discovering Low-Precision Networks Close to Full-Precision Networks for\n Efficient Embedded Inference
arXiv (Cornell University)·2018· 41 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
TrueNorth Ecosystem for Brain-Inspired Computing: Scalable Systems, Software, and Applications
2016· 60 citedOpen Access
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
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Frequent Co-authors

Dharmendra S. Modha(28), John V. Arthur(20), Myron Flickner(15), Jeffrey L. McKinstry(15), Andrew S. Cassidy(14), W. P. Risk(14), Rathinakumar Appuswamy(13), Brian Taba(13), Arnon Amir(13), Pallab Datta(13), Filipp Akopyan(12), Jun Sawada(11), Deepika Bablani(11), Michael DeBole(10), Tapan K. Nayak(10), Paul Merolla(9), Kai Schleupen(9), Alexander Andreopoulos(8), Rathinakumar Appuswamy(8), Yutaka Nakamura(7)