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Paul A. Merolla
Researcher at IBM
Publications - 82
Citations - 9106
Paul A. Merolla is an academic researcher from IBM. The author has contributed to research in topics: Neuromorphic engineering & TrueNorth. The author has an hindex of 25, co-authored 82 publications receiving 7298 citations. Previous affiliations of Paul A. Merolla include Stanford University & Oracle Corporation.
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Journal ArticleDOI
A million spiking-neuron integrated circuit with a scalable communication network and interface
Paul A. Merolla,John V. Arthur,Rodrigo Alvarez-Icaza,Andrew S. Cassidy,Jun Sawada,Filipp Akopyan,Bryan L. Jackson,Nabil Imam,Chen Guo,Yutaka Nakamura,Bernard Brezzo,Ivan Vo,Steven K. Esser,Rathinakumar Appuswamy,Brian Taba,Arnon Amir,Myron D. Flickner,William P. Risk,Rajit Manohar,Dharmendra S. Modha +19 more
TL;DR: Inspired by the brain’s structure, an efficient, scalable, and flexible non–von Neumann architecture is developed that leverages contemporary silicon technology and is well suited to many applications that use complex neural networks in real time, for example, multiobject detection and classification.
Journal ArticleDOI
TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip
Filipp Akopyan,Jun Sawada,Andrew S. Cassidy,Rodrigo Alvarez-Icaza,John V. Arthur,Paul A. Merolla,Nabil Imam,Yutaka Nakamura,Pallab Datta,Gi-Joon Nam,Brian Taba,Michael P. Beakes,Bernard Brezzo,Jente B. Kuang,Rajit Manohar,William P. Risk,Bryan L. Jackson,Dharmendra S. Modha +17 more
TL;DR: This work developed TrueNorth, a 65 mW real-time neurosynaptic processor that implements a non-von Neumann, low-power, highly-parallel, scalable, and defect-tolerant architecture, and successfully demonstrated the use of TrueNorth-based systems in multiple applications, including visual object recognition.
Journal ArticleDOI
Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations
Ben Varkey Benjamin,Peiran Gao,Emmett McQuinn,Swadesh Choudhary,Anand R. Chandrasekaran,Jean-Marie Bussat,Rodrigo Alvarez-Icaza,John V. Arthur,Paul A. Merolla,Kwabena Boahen +9 more
TL;DR: Neurogrid as discussed by the authors is a real-time neuromorphic system for simulating large-scale neural models in real time using 16 Neurocores, including axonal arbor, synapse, dendritic tree, and soma.
Journal ArticleDOI
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 Berg,Jeffrey L. McKinstry,Timothy Melano,R Davis,Carmelo di Nolfo,Pallab Datta,Arnon Amir,Brian Taba,Myron D. Flickner,Dharmendra S. Modha +15 more
TL;DR: This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.
Proceedings ArticleDOI
A digital neurosynaptic core using embedded crossbar memory with 45pJ per spike in 45nm
TL;DR: This work fabricated a key building block of a modular neuromorphic architecture, a neurosynaptic core, with 256 digital integrate-and-fire neurons and a 1024×256 bit SRAM crossbar memory for synapses using IBM's 45nm SOI process, leading to ultra-low active power consumption.